{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2026-04-06T14:01:22.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2026-04-06T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":42273,"title":"Form Blocks in a 1D array","description":"Lets say I have an array of size (10,1);\r\n\r\nI want to divide it into the num number of blocks which are given by user\r\n\r\nno_of_blocks=2;\r\n\r\noutput should be lower and upper limits of blocks\r\n\r\noutput_blocks=[1 5;6 10]\r\n\r\nanother example \r\n\r\narray of size (1,36)\r\ndivide into 7 blocks\r\n\r\n\r\noutput should be \r\n[1 7;\r\n8 14;\r\n15 21;\r\n22 28;\r\n29 35]\r\n\r\nthat is reject the remaining indices if it is not in the last block;\r\n\r\n\r\n\r\ninput arguments for the function are length of an array and no of blocks required.\r\n\r\n\r\n\r\n\r\n","description_html":"\u003cp\u003eLets say I have an array of size (10,1);\u003c/p\u003e\u003cp\u003eI want to divide it into the num number of blocks which are given by user\u003c/p\u003e\u003cp\u003eno_of_blocks=2;\u003c/p\u003e\u003cp\u003eoutput should be lower and upper limits of blocks\u003c/p\u003e\u003cp\u003eoutput_blocks=[1 5;6 10]\u003c/p\u003e\u003cp\u003eanother example\u003c/p\u003e\u003cp\u003earray of size (1,36)\r\ndivide into 7 blocks\u003c/p\u003e\u003cp\u003eoutput should be \r\n[1 7;\r\n8 14;\r\n15 21;\r\n22 28;\r\n29 35]\u003c/p\u003e\u003cp\u003ethat is reject the remaining indices if it is not in the last block;\u003c/p\u003e\u003cp\u003einput arguments for the function are length of an array and no of blocks required.\u003c/p\u003e","function_template":"function [limits_of_output_blocks] = divide_into_blocks(length_of_array,no_of_blocks)\r\n output_blocks = length_of_array;\r\nend\r\n\r\n\r\n\r\n","test_suite":"%%\r\n\r\ny_correct_1= [1 5 ;6 10];\r\noutput_blocks =divide_into_blocks(10,2);\r\n\r\nassert(isequal(output_blocks,y_correct_1))\r\n\r\n\r\n\r\n%%\r\n\r\ny_correct_1= [1 25 ;26 50;51 75 ;76 100];\r\n\r\n\r\noutput_blocks =divide_into_blocks(100,4);\r\n\r\nassert(isequal(output_blocks ,y_correct_1))\r\n\r\n\r\n%%\r\ny_correct_1= [1 8 ;9 16;17 24];\r\n\r\n\r\noutput_blocks =divide_into_blocks(24,3);\r\n\r\nassert(isequal(output_blocks ,y_correct_1))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":29722,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":31,"test_suite_updated_at":"2015-04-27T08:26:54.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2015-04-25T10:09:56.000Z","updated_at":"2025-05-07T10:41:05.000Z","published_at":"2015-04-25T10:25:15.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eLets say I have an array of size (10,1);\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI want to divide it into the num number of blocks which are given by user\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eno_of_blocks=2;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput should be lower and upper limits of blocks\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput_blocks=[1 5;6 10]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eanother example\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003earray of size (1,36) divide into 7 blocks\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput should be [1 7; 8 14; 15 21; 22 28; 29 35]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethat is reject the remaining indices if it is not in the last block;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003einput arguments for the function are length of an array and no of blocks required.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" 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The elements of the (j,k)th block all have the same value: (j+k-1).\r\nFor example, if m = 4, n = 3, p = 2, and q = 3, the matrix is:\r\n\r\nYou can assume m, n, p, and q are all positive integers. (They can have the value 1, however.)","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 590.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 295.25px; transform-origin: 407px 295.25px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 381px 8px; transform-origin: 381px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven m, n, p, and q, create a matrix of m-by-n blocks (submatrices), each sized p-by-q. The elements of the (j,k)th block all have the same value: (j+k-1).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 183.5px 8px; transform-origin: 183.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, if m = 4, n = 3, p = 2, and q = 3, the matrix 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\" alt=\"An 8-by-9 matrix with each 2-by-3 block shaded a different color. The 4 (m) vertical and 3 (n) horizontal blocks are noted. The 2-by-3 (p-by-q) size of each block is also noted.\" data-image-state=\"image-loaded\" width=\"688\" height=\"474\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 297.5px 8px; transform-origin: 297.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou can assume m, n, p, and q are all positive integers. (They can have the value 1, however.)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function A = blockjplusk1(m,n,p,q)\r\nA = m+n+p+q;\r\nend","test_suite":"    %%\r\n    A = blockjplusk1(6,1,2,3);\r\n    Asol = [1 1 1;1 1 1;2 2 2;2 2 2;3 3 3;3 3 3;4 4 4;4 4 4;5 5 5;5 5 5;6 6 6;6 6 6];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk1(6,7,3,2);\r\n    Asol = [1 1 2 2 3 3 4 4 5 5 6 6 7 7;1 1 2 2 3 3 4 4 5 5 6 6 7 7;1 1 2 2 3 3 4 4 5 5 6 6 7 7;2 2 3 3 4 4 5 5 6 6 7 7 8 8;2 2 3 3 4 4 5 5 6 6 7 7 8 8;2 2 3 3 4 4 5 5 6 6 7 7 8 8;3 3 4 4 5 5 6 6 7 7 8 8 9 9;3 3 4 4 5 5 6 6 7 7 8 8 9 9;3 3 4 4 5 5 6 6 7 7 8 8 9 9;4 4 5 5 6 6 7 7 8 8 9 9 10 10;4 4 5 5 6 6 7 7 8 8 9 9 10 10;4 4 5 5 6 6 7 7 8 8 9 9 10 10;5 5 6 6 7 7 8 8 9 9 10 10 11 11;5 5 6 6 7 7 8 8 9 9 10 10 11 11;5 5 6 6 7 7 8 8 9 9 10 10 11 11;6 6 7 7 8 8 9 9 10 10 11 11 12 12;6 6 7 7 8 8 9 9 10 10 11 11 12 12;6 6 7 7 8 8 9 9 10 10 11 11 12 12];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk1(3,1,1,4);\r\n    Asol = [1 1 1 1;2 2 2 2;3 3 3 3];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk1(2,4,2,3);\r\n    Asol = [1 1 1 2 2 2 3 3 3 4 4 4;1 1 1 2 2 2 3 3 3 4 4 4;2 2 2 3 3 3 4 4 4 5 5 5;2 2 2 3 3 3 4 4 4 5 5 5];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk1(8,3,3,1);\r\n    Asol = [1 2 3;1 2 3;1 2 3;2 3 4;2 3 4;2 3 4;3 4 5;3 4 5;3 4 5;4 5 6;4 5 6;4 5 6;5 6 7;5 6 7;5 6 7;6 7 8;6 7 8;6 7 8;7 8 9;7 8 9;7 8 9;8 9 10;8 9 10;8 9 10];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    m = randi(8);\r\n    n = randi(8);\r\n    p = randi(4);\r\n    q = randi(4);\r\n    A = blockjplusk1(m,n,p,q);\r\n    assert( isequal(size(A),[m*p,n*q]) )","published":true,"deleted":false,"likes_count":4,"comments_count":0,"created_by":287,"edited_by":287,"edited_at":"2022-10-24T13:13:51.000Z","deleted_by":null,"deleted_at":null,"solvers_count":119,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-07-28T17:16:12.000Z","updated_at":"2026-03-11T20:36:24.000Z","published_at":"2022-10-24T13:13:51.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven m, n, p, and q, create a matrix of m-by-n blocks (submatrices), each sized p-by-q. The elements of the (j,k)th block all have the same value: (j+k-1).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if m = 4, n = 3, p = 2, and q = 3, the matrix is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"474\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"688\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"An 8-by-9 matrix with each 2-by-3 block shaded a different color. The 4 (m) vertical and 3 (n) horizontal blocks are noted. The 2-by-3 (p-by-q) size of each block is also noted.\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can assume m, n, p, and q are all positive integers. (They can have the value 1, 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":55260,"title":"Create block matrix of integers (j+k-1) - Part II","description":"Given m, n, p, and q, create an m-by-n matrix made up of submatrices, each sized p-by-q (if possible - the last row and column of blocks may be smaller). The elements of the (j,k)th block all have the same value: (j+k-1).\r\nFor example, if m = 4, n = 7, p = 2, and q = 3, the matrix is:\r\n\r\nYou can assume m, n, p, and q are all positive integers. (They can have the value 1, however.) As in the illustration above, m may or may not be divisible by p, and n may or may not be divisible by q. It is even possible for m \u003c p or n \u003c q. The resulting matrix will always be m-by-n.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 412.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 206.25px; transform-origin: 407px 206.25px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374.5px 8px; transform-origin: 374.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven m, n, p, and q, create an m-by-n matrix made up of submatrices, each sized p-by-q (if possible - the last row and column of blocks may be smaller). The elements of the (j,k)th block all have the same value: (j+k-1).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 183.5px 8px; transform-origin: 183.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, if m = 4, n = 7, p = 2, and q = 3, the matrix 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\" alt=\"A 4-by-7 matrix with each 2-by-3 block shaded a different color. The overall dimensions (m = 4, n = 7) of the matrix are noted. The 2-by-3 (p-by-q) size of each block is also noted.\" data-image-state=\"image-loaded\" width=\"454\" height=\"254\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 363px 8px; transform-origin: 363px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou can assume m, n, p, and q are all positive integers. (They can have the value 1, however.) As in the illustration above, m may or may not be divisible by p, and n may or may not be divisible by q. It is even possible for m \u0026lt; p or n \u0026lt; q. The resulting matrix will always be m-by-n.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function A = blockjplusk2(m,n,p,q)\r\nA = m+n+p+q;\r\nend","test_suite":"    %%\r\n    A = blockjplusk2(8,8,3,1);\r\n    Asol = [1 2 3 4 5 6 7 8;1 2 3 4 5 6 7 8;1 2 3 4 5 6 7 8;2 3 4 5 6 7 8 9;2 3 4 5 6 7 8 9;2 3 4 5 6 7 8 9;3 4 5 6 7 8 9 10;3 4 5 6 7 8 9 10];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk2(2,3,4,2);\r\n    Asol = [1 1 2;1 1 2];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk2(3,7,3,3);\r\n    Asol = [1 1 1 2 2 2 3;1 1 1 2 2 2 3;1 1 1 2 2 2 3];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk2(8,3,4,4);\r\n    Asol = [1 1 1;1 1 1;1 1 1;1 1 1;2 2 2;2 2 2;2 2 2;2 2 2];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk2(4,5,1,1);\r\n    Asol = [1 2 3 4 5;2 3 4 5 6;3 4 5 6 7;4 5 6 7 8];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk2(5,7,4,1);\r\n    Asol = [1 2 3 4 5 6 7;1 2 3 4 5 6 7;1 2 3 4 5 6 7;1 2 3 4 5 6 7;2 3 4 5 6 7 8];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk2(5,4,1,2);\r\n    Asol = [1 1 2 2;2 2 3 3;3 3 4 4;4 4 5 5;5 5 6 6];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    m = randi(8);\r\n    n = randi(8);\r\n    p = randi(4);\r\n    q = randi(4);\r\n    A = blockjplusk2(m,n,p,q);\r\n    assert( isequal(size(A),[m,n]) \u0026\u0026 (A(1,1)==1) \u0026\u0026 isequal(A(end,end),ceil(n/q)+ceil(m/p)-1) )","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":287,"edited_by":287,"edited_at":"2022-10-24T13:15:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":97,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-07-28T17:39:32.000Z","updated_at":"2026-03-11T20:39:48.000Z","published_at":"2022-10-24T13:15:00.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven m, n, p, and q, create an m-by-n matrix made up of submatrices, each sized p-by-q (if possible - the last row and column of blocks may be smaller). The elements of the (j,k)th block all have the same value: (j+k-1).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if m = 4, n = 7, p = 2, and q = 3, the matrix is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"254\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"454\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"A 4-by-7 matrix with each 2-by-3 block shaded a different color. The overall dimensions (m = 4, n = 7) of the matrix are noted. The 2-by-3 (p-by-q) size of each block is also noted.\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can assume m, n, p, and q are all positive integers. (They can have the value 1, however.) As in the illustration above, m may or may not be divisible by p, and n may or may not be divisible by q. It is even possible for m \u0026lt; p or n \u0026lt; q. The resulting matrix will always be 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this tower of blocks going to fall?\r\n\r\n\u003c\u003chttp://www.alfnie.com/software/equilibrium02.jpg\u003e\u003e\r\n\r\n*Description*\r\n\r\nGiven a stacking configuration for a series of square blocks, your function should return _true_ if they are at equilibrium and _false_ otherwise. \r\n\r\nThe block configuration for N blocks is provided as a input vector *x* with N elements listing the _x-coordinates_ of the left-side of each block. The blocks are square with side equal to 1 (so the i-th block left side is at x(i) and its right side is at x(i)+1). The _y-coordinates_ of each block are determined implicitly by the order of the blocks, which are dropped \"tetris-style\" until they hit the floor or another block. \r\n\r\nAll blocks are identical (same dimensions and mass) and perfectly smooth (friction is to be disregarded).\r\n \r\nIntermediate positions may be unstable. You are only required to determine whether the final configuration is stable.\r\n\r\n*Examples*:\r\n\r\nExample (1) \r\n \r\n x = [0 0.4];\r\n\r\n\u003c\u003chttp://www.alfnie.com/software/equilibrium01a.jpg\u003e\u003e\r\n\r\nThe first block bottom-left corner is at (0,0) and the second block falls on top of it, with its bottom-left corner at (0.4 1). This configuration is stable so your function should return _true_.\r\n\r\nExample (2) \r\n\r\n x = [0 0.6];\r\n\r\n\u003c\u003chttp://www.alfnie.com/software/equilibrium01b.jpg\u003e\u003e\r\n\r\nThe first block bottom-left corner is at (0,0) and the second block falls on top of it, with its bottom-left corner at (0.6 1). This configuration is unstable (the second block will fall) so your function should return _false_.\r\n\r\nExample (3) \r\n\r\n x = [0 1.5 0.6];\r\n\r\n\u003c\u003chttp://www.alfnie.com/software/equilibrium01c.jpg\u003e\u003e\r\n\r\nThe three block bottom-left corner coordinates are (0,0) (1.5,0) and (0.6,1). This configuration is stable so your function should return _true_.\r\n\r\nExample (4) \r\n\r\n x = [0 .9 -.9 zeros(1,5)];\r\n\r\n\u003c\u003chttp://www.alfnie.com/software/equilibrium01d.jpg\u003e\u003e\r\n\r\nThis configuration is unstable, but note that if instead of five we add a few more blocks on top of this at the 0 position that will keep the tower from falling!\r\n\r\nExample (5) \r\n\r\nx = cumsum(fliplr(1./(1:8))/2);\r\n\r\n\u003c\u003chttp://www.alfnie.com/software/equilibrium01e.jpg\u003e\u003e\r\n\r\nThis configuration is stable (see the \u003chttp://en.wikipedia.org/wiki/Block-stacking_problem classic optimal stacking solution\u003e) so your function should return _true_.\r\n\r\n*Display*\r\n\r\nIf you wish, you may display any given block configuration *x* using the code below:\r\n\r\n clf;\r\n y=[];\r\n for n=1:numel(x), y(n)=max([0 y(abs(x(1:n-1)-x(n))\u003c1)+1]); end\r\n h=arrayfun(@(x,y)patch(x+[0,1,1,0],y+[0,0,1,1],rand(1,3)),x,y);\r\n text(x+.5,y+.5,arrayfun(@num2str,1:numel(x),'uni',0),...\r\n 'horizontalalignment','center');\r\n\r\nVisit \u003chttp://www.mathworks.com/matlabcentral/cody/problems/1910-block-canvas Block canvas\u003e for a related Cody problem.","description_html":"\u003cp\u003eIs this tower of blocks going to fall?\u003c/p\u003e\u003cimg src = \"http://www.alfnie.com/software/equilibrium02.jpg\"\u003e\u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven a stacking configuration for a series of square blocks, your function should return \u003ci\u003etrue\u003c/i\u003e if they are at equilibrium and \u003ci\u003efalse\u003c/i\u003e otherwise.\u003c/p\u003e\u003cp\u003eThe block configuration for N blocks is provided as a input vector \u003cb\u003ex\u003c/b\u003e with N elements listing the \u003ci\u003ex-coordinates\u003c/i\u003e of the left-side of each block. The blocks are square with side equal to 1 (so the i-th block left side is at x(i) and its right side is at x(i)+1). The \u003ci\u003ey-coordinates\u003c/i\u003e of each block are determined implicitly by the order of the blocks, which are dropped \"tetris-style\" until they hit the floor or another block.\u003c/p\u003e\u003cp\u003eAll blocks are identical (same dimensions and mass) and perfectly smooth (friction is to be disregarded).\u003c/p\u003e\u003cp\u003eIntermediate positions may be unstable. You are only required to determine whether the final configuration is stable.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExamples\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eExample (1)\u003c/p\u003e\u003cpre\u003e x = [0 0.4];\u003c/pre\u003e\u003cimg src = \"http://www.alfnie.com/software/equilibrium01a.jpg\"\u003e\u003cp\u003eThe first block bottom-left corner is at (0,0) and the second block falls on top of it, with its bottom-left corner at (0.4 1). This configuration is stable so your function should return \u003ci\u003etrue\u003c/i\u003e.\u003c/p\u003e\u003cp\u003eExample (2)\u003c/p\u003e\u003cpre\u003e x = [0 0.6];\u003c/pre\u003e\u003cimg src = \"http://www.alfnie.com/software/equilibrium01b.jpg\"\u003e\u003cp\u003eThe first block bottom-left corner is at (0,0) and the second block falls on top of it, with its bottom-left corner at (0.6 1). This configuration is unstable (the second block will fall) so your function should return \u003ci\u003efalse\u003c/i\u003e.\u003c/p\u003e\u003cp\u003eExample (3)\u003c/p\u003e\u003cpre\u003e x = [0 1.5 0.6];\u003c/pre\u003e\u003cimg src = \"http://www.alfnie.com/software/equilibrium01c.jpg\"\u003e\u003cp\u003eThe three block bottom-left corner coordinates are (0,0) (1.5,0) and (0.6,1). This configuration is stable so your function should return \u003ci\u003etrue\u003c/i\u003e.\u003c/p\u003e\u003cp\u003eExample (4)\u003c/p\u003e\u003cpre\u003e x = [0 .9 -.9 zeros(1,5)];\u003c/pre\u003e\u003cimg src = \"http://www.alfnie.com/software/equilibrium01d.jpg\"\u003e\u003cp\u003eThis configuration is unstable, but note that if instead of five we add a few more blocks on top of this at the 0 position that will keep the tower from falling!\u003c/p\u003e\u003cp\u003eExample (5)\u003c/p\u003e\u003cp\u003ex = cumsum(fliplr(1./(1:8))/2);\u003c/p\u003e\u003cimg src = \"http://www.alfnie.com/software/equilibrium01e.jpg\"\u003e\u003cp\u003eThis configuration is stable (see the \u003ca href = \"http://en.wikipedia.org/wiki/Block-stacking_problem\"\u003eclassic optimal stacking solution\u003c/a\u003e) so your function should return \u003ci\u003etrue\u003c/i\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDisplay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIf you wish, you may display any given block configuration \u003cb\u003ex\u003c/b\u003e using the code below:\u003c/p\u003e\u003cpre\u003e clf;\r\n y=[];\r\n for n=1:numel(x), y(n)=max([0 y(abs(x(1:n-1)-x(n))\u0026lt;1)+1]); end\r\n h=arrayfun(@(x,y)patch(x+[0,1,1,0],y+[0,0,1,1],rand(1,3)),x,y);\r\n text(x+.5,y+.5,arrayfun(@num2str,1:numel(x),'uni',0),...\r\n 'horizontalalignment','center');\u003c/pre\u003e\u003cp\u003eVisit \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/1910-block-canvas\"\u003eBlock canvas\u003c/a\u003e for a related Cody problem.\u003c/p\u003e","function_template":"function y = equilibrium(x)\r\n  y = true;\r\nend","test_suite":"%%\r\nx = [0 0.6];\r\nassert(isequal(equilibrium(x),false))\r\n\r\n%%\r\nx = [0 0.4];\r\nassert(isequal(equilibrium(x),true))\r\n\r\n%%\r\nx = [0 1.5 0.6];\r\nassert(isequal(equilibrium(x),true))\r\n\r\n%%\r\nx = cumsum(fliplr(1./(1:16))/2);\r\nassert(isequal(equilibrium(x),true))\r\n\r\n%%\r\nx = [1.5 2.5 1 0.25 5.5 3.5 -1.5 -0.25 -4 -1.75 6.25 -1.25 0.5 1 -0.5 4.75 -1.25 -1.5 0.5 1.5];\r\nassert(isequal(equilibrium(x),false))\r\n\r\n%%\r\nx = [-1.5 -0.25 -0.25 -1.75 1.75 -2 -5.25 2.25 0.75 -0 0.25 0 -1 -1.5 4.75 -1 4.75 -2.5 3.25 -1];\r\nassert(isequal(equilibrium(x),true))\r\n\r\n%%\r\nx = [1.25 -1.75 -0.75 2.75 -1.5 3.75 1.75 1.5 -0.25 -4 -0.5 -2 1.75 -3.5 -2 -3.75 0 -1.75 1.75 3.25 -1.5 -0.5 1.25 -2 1.5 3 -0.25 1.75 -0.5 2.75 0.5 -4.25 1.5 5.5 3 4.25 2.75 -0.75 0.5 3 3.5 3.25 1.75 1.5 3.25 2.5 5.5 -2 -3.75 -1 5 0.25 -3.75 5.5 1.75 2 1.75 0.5 -3.75 1.5 -0 2 0.5 0 -0.25 0.25 -9 1.75 -3.75 1.25 -3.75 -0 1.75 3.5 -3.75 3.75 -5.75 1.25 3.5 1.5 -2 2 -2.5 -1.5 3 1.75 -1.5 3.25 1.75 -1.5 1.25 -2.5 1.25 -4.25 3.25 -2.5 1.75 1.75 7.25 3.5];\r\nassert(isequal(equilibrium(x),true))\r\n\r\n%%\r\nx = [-0.5 1.5 3.5 0.5 -5.75 2 -1.5 0.25 -0.25 -3 -0.25 3 -3.5 -4.5 1.75 -0.25 0.75 3 0.25 -2.5 2.25 -0.25 1.75 -1.5 -5 -0 -0.5 -2 -0 4.75 -0 2 3.5 1.5 -2.25 3.5 -0.5 4.5 2.5 0.5 1.75 3.5 -0 -2.25 -0.25 -4.75 -2.5 -0.75 -6 2.75 -5 2.25 1 -2.25 -0.75 -0.25 -3.5 0.75 -0 -0.5 -0.5 -1.75 -2 -0.25 -0.25 5 -0.25 -0.75 -0.25 -5 -2 -0.25 -5.5 -5 -0.5 -2 1 -0.75 2 3.25 4.5 2.25 1.25 -0.25 -0.5 -0.25 -2.5 -5 2.25 -2 7.5 6.5 2.25 -0.25 -0.5 7.25 -2.5 1 -2.5 -4.75];\r\nassert(isequal(equilibrium(x),false))\r\n\r\n%%\r\nx = [0.1 0.1 -4.6 -0.4 -1.5 1.6 3 2.7 2.3 -2.7 0.1 -1.7 0 4.4 3.8 -0.4 -2 -0.6 3.3 2.5 -3 -1.7 3.1 2.7 2.7 3.1 -0.4 1.1 -0.2 -0.1 -0.3 2.7];\r\nassert(isequal(equilibrium(x),true))\r\n\r\n%%\r\nx = [0.1 0.1 -4.6 -0.4 -1.5 1.6 3 2.7 2.3 -2.7 0.1 -1.7 0 4.4 3.8 -0.4 -2 -0.6 3.3 2.5 -3 -1.7 3.1 2.7 2.7 3.1 -0.4 1.1 -0.2 -0.1 -0.3 2.7 -1.8 2.3];\r\nassert(isequal(equilibrium(x),false))\r\n\r\n%%\r\nx = [-3.5 2.6 -0.7 -1.1 -2.6 -2 0.8 -0.7 2.7 0.4 -5 3.7 -1.2 -1.3 2.8 0.8 -1.5 -1.8 0 -0 4.8 -1.4 -1.2 -1.5 1 0.2 2.6 1.7 1.6 -1.3 2.1 -1.5 -1.4 2 0.1 -0.1 -0.1 4.6 -3 -0.3 0.2 -1.9 -0 0.1 0 2.1 -1.7 -3.1 -0 0.2 -0.1 -0.5 4.7 -1.8 -0.1 -2.2];\r\nassert(isequal(equilibrium(x),false));\r\n\r\n%%\r\nx = [-2.5 2.6 -0.7 -1.1 -2.6 -2 0.8 -0.7 2.7 0.4 -5 3.7 -1.2 -1.3 2.8 0.8 -1.5 -1.8 0 -0 4.8 -1.4 -1.2 -1.5 1 0.2 2.6 1.7 1.6 -1.3 2.1 -1.5 -1.4 2 0.1 -0.1 -0.1 4.6 -3 -0.3 0.2 -1.9 -0 0.1 0 2.1 -1.7 -3.1 -0 0.2 -0.1 -0.5 4.7 -1.8 -0.1 -2.2];\r\nassert(isequal(equilibrium(x),true));\r\n\r\n%%\r\nx =[0 .9 -.9 zeros(1,8)];\r\nassert(isequal(equilibrium(x),true))\r\n\r\n%%\r\nx =[0 .9 -.9 zeros(1,6)];\r\nassert(isequal(equilibrium(x),false))\r\n\r\n%%\r\nx = repmat([0 .7 -.7 0],1,2);\r\nassert(isequal(equilibrium(x),false))\r\n\r\n%%\r\nx = repmat([0 .6 -.6 0],1,2);\r\nassert(isequal(equilibrium(x),true))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":43,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-10-02T08:49:59.000Z","updated_at":"2013-10-08T00:22:34.000Z","published_at":"2013-10-02T09:44:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/media/image2.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId3\",\"target\":\"/media/image3.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId4\",\"target\":\"/media/image4.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId5\",\"target\":\"/media/image5.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId6\",\"target\":\"/media/image6.JPEG\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIs this tower of blocks going to fall?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eDescription\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a stacking configuration for a series of square blocks, your function should return\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etrue\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e if they are at equilibrium and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efalse\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e otherwise.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe block configuration for N blocks is provided as a input vector\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e with N elements listing the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ex-coordinates\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of the left-side of each block. The blocks are square with side equal to 1 (so the i-th block left side is at x(i) and its right side is at x(i)+1). The\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ey-coordinates\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of each block are determined implicitly by the order of the blocks, which are dropped \\\"tetris-style\\\" until they hit the floor or another block.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAll blocks are identical (same dimensions and mass) and perfectly smooth (friction is to be disregarded).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIntermediate positions may be unstable. You are only required to determine whether the final configuration is stable.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExamples\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample (1)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x = [0 0.4];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe first block bottom-left corner is at (0,0) and the second block falls on top of it, with its bottom-left corner at (0.4 1). This configuration is stable so your function should return\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etrue\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample (2)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x = [0 0.6];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId3\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe first block bottom-left corner is at (0,0) and the second block falls on top of it, with its bottom-left corner at (0.6 1). This configuration is unstable (the second block will fall) so your function should return\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efalse\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample (3)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x = [0 1.5 0.6];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId4\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe three block bottom-left corner coordinates are (0,0) (1.5,0) and (0.6,1). This configuration is stable so your function should return\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etrue\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample (4)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x = [0 .9 -.9 zeros(1,5)];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId5\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis configuration is unstable, but note that if instead of five we add a few more blocks on top of this at the 0 position that will keep the tower from falling!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample (5)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ex = cumsum(fliplr(1./(1:8))/2);\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId6\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis configuration is stable (see the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Block-stacking_problem\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eclassic optimal stacking solution\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e) so your function should return\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etrue\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eDisplay\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf you wish, you may display any given block configuration\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e using the code below:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ clf;\\n y=[];\\n for n=1:numel(x), y(n)=max([0 y(abs(x(1:n-1)-x(n))\u003c1)+1]); end\\n h=arrayfun(@(x,y)patch(x+[0,1,1,0],y+[0,0,1,1],rand(1,3)),x,y);\\n text(x+.5,y+.5,arrayfun(@num2str,1:numel(x),'uni',0),...\\n 'horizontalalignment','center');]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVisit\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/1910-block-canvas\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eBlock canvas\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for a related Cody problem.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray 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Blocks in a 1D array","description":"Lets say I have an array of size (10,1);\r\n\r\nI want to divide it into the num number of blocks which are given by user\r\n\r\nno_of_blocks=2;\r\n\r\noutput should be lower and upper limits of blocks\r\n\r\noutput_blocks=[1 5;6 10]\r\n\r\nanother example \r\n\r\narray of size (1,36)\r\ndivide into 7 blocks\r\n\r\n\r\noutput should be \r\n[1 7;\r\n8 14;\r\n15 21;\r\n22 28;\r\n29 35]\r\n\r\nthat is reject the remaining indices if it is not in the last block;\r\n\r\n\r\n\r\ninput arguments for the function are length of an array and no of blocks required.\r\n\r\n\r\n\r\n\r\n","description_html":"\u003cp\u003eLets say I have an array of size (10,1);\u003c/p\u003e\u003cp\u003eI want to divide it into the num number of blocks which are given by user\u003c/p\u003e\u003cp\u003eno_of_blocks=2;\u003c/p\u003e\u003cp\u003eoutput should be lower and upper limits of blocks\u003c/p\u003e\u003cp\u003eoutput_blocks=[1 5;6 10]\u003c/p\u003e\u003cp\u003eanother example\u003c/p\u003e\u003cp\u003earray of size (1,36)\r\ndivide into 7 blocks\u003c/p\u003e\u003cp\u003eoutput should be \r\n[1 7;\r\n8 14;\r\n15 21;\r\n22 28;\r\n29 35]\u003c/p\u003e\u003cp\u003ethat is reject the remaining indices if it is not in the last block;\u003c/p\u003e\u003cp\u003einput arguments for the function are length of an array and no of blocks required.\u003c/p\u003e","function_template":"function [limits_of_output_blocks] = divide_into_blocks(length_of_array,no_of_blocks)\r\n output_blocks = length_of_array;\r\nend\r\n\r\n\r\n\r\n","test_suite":"%%\r\n\r\ny_correct_1= [1 5 ;6 10];\r\noutput_blocks =divide_into_blocks(10,2);\r\n\r\nassert(isequal(output_blocks,y_correct_1))\r\n\r\n\r\n\r\n%%\r\n\r\ny_correct_1= [1 25 ;26 50;51 75 ;76 100];\r\n\r\n\r\noutput_blocks =divide_into_blocks(100,4);\r\n\r\nassert(isequal(output_blocks ,y_correct_1))\r\n\r\n\r\n%%\r\ny_correct_1= [1 8 ;9 16;17 24];\r\n\r\n\r\noutput_blocks =divide_into_blocks(24,3);\r\n\r\nassert(isequal(output_blocks ,y_correct_1))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":29722,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":31,"test_suite_updated_at":"2015-04-27T08:26:54.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2015-04-25T10:09:56.000Z","updated_at":"2025-05-07T10:41:05.000Z","published_at":"2015-04-25T10:25:15.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eLets say I have an array of size (10,1);\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI want to divide it into the num number of blocks which are given by user\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eno_of_blocks=2;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput should be lower and upper limits of blocks\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput_blocks=[1 5;6 10]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eanother example\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003earray of size (1,36) divide into 7 blocks\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput should be [1 7; 8 14; 15 21; 22 28; 29 35]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethat is reject the remaining indices if it is not in the last block;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003einput arguments for the function are length of an array and no of blocks required.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" 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The elements of the (j,k)th block all have the same value: (j+k-1).\r\nFor example, if m = 4, n = 3, p = 2, and q = 3, the matrix is:\r\n\r\nYou can assume m, n, p, and q are all positive integers. (They can have the value 1, however.)","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 590.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 295.25px; transform-origin: 407px 295.25px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 381px 8px; transform-origin: 381px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven m, n, p, and q, create a matrix of m-by-n blocks (submatrices), each sized p-by-q. The elements of the (j,k)th block all have the same value: (j+k-1).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 183.5px 8px; transform-origin: 183.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, if m = 4, n = 3, p = 2, and q = 3, the matrix 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\" alt=\"An 8-by-9 matrix with each 2-by-3 block shaded a different color. The 4 (m) vertical and 3 (n) horizontal blocks are noted. The 2-by-3 (p-by-q) size of each block is also noted.\" data-image-state=\"image-loaded\" width=\"688\" height=\"474\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 297.5px 8px; transform-origin: 297.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou can assume m, n, p, and q are all positive integers. (They can have the value 1, however.)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function A = blockjplusk1(m,n,p,q)\r\nA = m+n+p+q;\r\nend","test_suite":"    %%\r\n    A = blockjplusk1(6,1,2,3);\r\n    Asol = [1 1 1;1 1 1;2 2 2;2 2 2;3 3 3;3 3 3;4 4 4;4 4 4;5 5 5;5 5 5;6 6 6;6 6 6];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk1(6,7,3,2);\r\n    Asol = [1 1 2 2 3 3 4 4 5 5 6 6 7 7;1 1 2 2 3 3 4 4 5 5 6 6 7 7;1 1 2 2 3 3 4 4 5 5 6 6 7 7;2 2 3 3 4 4 5 5 6 6 7 7 8 8;2 2 3 3 4 4 5 5 6 6 7 7 8 8;2 2 3 3 4 4 5 5 6 6 7 7 8 8;3 3 4 4 5 5 6 6 7 7 8 8 9 9;3 3 4 4 5 5 6 6 7 7 8 8 9 9;3 3 4 4 5 5 6 6 7 7 8 8 9 9;4 4 5 5 6 6 7 7 8 8 9 9 10 10;4 4 5 5 6 6 7 7 8 8 9 9 10 10;4 4 5 5 6 6 7 7 8 8 9 9 10 10;5 5 6 6 7 7 8 8 9 9 10 10 11 11;5 5 6 6 7 7 8 8 9 9 10 10 11 11;5 5 6 6 7 7 8 8 9 9 10 10 11 11;6 6 7 7 8 8 9 9 10 10 11 11 12 12;6 6 7 7 8 8 9 9 10 10 11 11 12 12;6 6 7 7 8 8 9 9 10 10 11 11 12 12];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk1(3,1,1,4);\r\n    Asol = [1 1 1 1;2 2 2 2;3 3 3 3];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk1(2,4,2,3);\r\n    Asol = [1 1 1 2 2 2 3 3 3 4 4 4;1 1 1 2 2 2 3 3 3 4 4 4;2 2 2 3 3 3 4 4 4 5 5 5;2 2 2 3 3 3 4 4 4 5 5 5];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk1(8,3,3,1);\r\n    Asol = [1 2 3;1 2 3;1 2 3;2 3 4;2 3 4;2 3 4;3 4 5;3 4 5;3 4 5;4 5 6;4 5 6;4 5 6;5 6 7;5 6 7;5 6 7;6 7 8;6 7 8;6 7 8;7 8 9;7 8 9;7 8 9;8 9 10;8 9 10;8 9 10];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    m = randi(8);\r\n    n = randi(8);\r\n    p = randi(4);\r\n    q = randi(4);\r\n    A = blockjplusk1(m,n,p,q);\r\n    assert( isequal(size(A),[m*p,n*q]) )","published":true,"deleted":false,"likes_count":4,"comments_count":0,"created_by":287,"edited_by":287,"edited_at":"2022-10-24T13:13:51.000Z","deleted_by":null,"deleted_at":null,"solvers_count":119,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-07-28T17:16:12.000Z","updated_at":"2026-03-11T20:36:24.000Z","published_at":"2022-10-24T13:13:51.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven m, n, p, and q, create a matrix of m-by-n blocks (submatrices), each sized p-by-q. The elements of the (j,k)th block all have the same value: (j+k-1).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if m = 4, n = 3, p = 2, and q = 3, the matrix is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"474\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"688\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"An 8-by-9 matrix with each 2-by-3 block shaded a different color. The 4 (m) vertical and 3 (n) horizontal blocks are noted. The 2-by-3 (p-by-q) size of each block is also noted.\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can assume m, n, p, and q are all positive integers. (They can have the value 1, 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":55260,"title":"Create block matrix of integers (j+k-1) - Part II","description":"Given m, n, p, and q, create an m-by-n matrix made up of submatrices, each sized p-by-q (if possible - the last row and column of blocks may be smaller). The elements of the (j,k)th block all have the same value: (j+k-1).\r\nFor example, if m = 4, n = 7, p = 2, and q = 3, the matrix is:\r\n\r\nYou can assume m, n, p, and q are all positive integers. (They can have the value 1, however.) As in the illustration above, m may or may not be divisible by p, and n may or may not be divisible by q. It is even possible for m \u003c p or n \u003c q. The resulting matrix will always be m-by-n.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 412.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 206.25px; transform-origin: 407px 206.25px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374.5px 8px; transform-origin: 374.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven m, n, p, and q, create an m-by-n matrix made up of submatrices, each sized p-by-q (if possible - the last row and column of blocks may be smaller). The elements of the (j,k)th block all have the same value: (j+k-1).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 183.5px 8px; transform-origin: 183.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, if m = 4, n = 7, p = 2, and q = 3, the matrix 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wPLdKoacr5eWw7vKYxVDZoLxh/2nKJWxavuNYKQ/0ca4syY2J2R/Zeyqh8wEYznD41Xm4Gv9sQIbPschrnzITDCWMzx2wVQwcpmCjXF3q8hP/nH5RW9l7MqHjATj2JT2nmG+OlgOl6kvY6sYct6T2mrIaDA+9pbhn8pj5XARwUh/co2xvDnqaDhV+VVnZWw3cq5xyEgwzotf5fCeedPId+V4kfoytoohD0ffPOTydzzPBGOphY+dYd6XWg4XEYz0J9cYb1/Of7KJ2PLrzsrYtQ8ZCcb57aTL4T2Hq9qR6svYKoY8nN7zkOcOxm+/ffvxX75/NPz+aTl7OVxEMNKfBo1xegUvB52VsWsfMrPG+Bf/2+c3b960LGOBWwTXMeSXI4Y8fzDuZY2RyxRvjGPJGUxHnZaxqx0yE4wHHa5qR0rcInjIlQzZPBjLyas+llEw0p9wY5xfzLsuY1c85AmCMbLhs3XmXMmQrYNx3ntTdXLBSH+ijfH2xbzjMnbVQ7YPxnlhrO7t1uqX7w66liEbB+O7cu6qJUbBSIeCjfFnd05QTtpbGbvyIdsHY+D9Wgb1Gz4PupYhGwfj/I5wD98S5zkEI/3JNcZ7ykk7K2P3jCNe05Dtg7GcvfL0jcvYOOHg0odsGozbvuhjp7g4ucZ4TzlpZ2XsnnHEaxqyeTDOd+bVrd41LmNXM2TDYHw718XqUwtG+qMxDjTGlHldrPbsTcvY9QzZKBh/fDu/fepG7ZkFI/3RGAcaY8q8kbJuV0vmow73up4hWwRjOd9W9YkFI/3RGAcaY8j8iRYV96RPWt4JcUVDtg/GqjsYJ4KR/miMA40xY/tqXrfbc9Awc65pyAbBuP00/1HdZ/dPBCP90RgHGmPEd+XMlR9zuDGeqknmXNWQDYLxx3K+2Y1LqVwgjXGgMSaMBWpUf+pmGz6va8jmjXGjtjUKRvqjMQ40xoBty0mcuVUZu7IhGwTj7n7U4v335bFlBCP90RgHGmO921fzqk+sKBpt+Ly2IRsE46ePb8dTfbq9j/H16x/HhxYSjPRHYxxojNVuyllD522zr2UVQ2733QSGbBCMO+a3EPexU1wcjXGgMdaa77rLVLFGmbOKIW9zMTBk22DcefebmnVGwUh/NMaBxlgpXMXaLN9d4ZCNg/H2bcRrTi8Y6Y/GONAY66SrWJMNn9c4ZPNg3N7uX1EZBSP90RgHGmON280i4XMmM+c6h2wfjF/L6Ss+klEw0h+NcaAxVth5NU9VsfyGzysdsn0wbi+m/lCOn08w0h+NcaAxLvexnG4Qq2LxfS2rGHL7OVO5IU8QjPPW1OVvmioY6Y/GONAYF7tdFKv9rIo7spmziiFvt6PmhjxBMM6rjMvv2BCM9EdjHGiMS91uoqx/69Fd41XFVOZc75CCERbRGAca40K3VSwbOdENn1c85CmCsSwyCkYuicY40BgX2dkskloUm+XK2FUPecJgXL4tVTDSH41xoDEusfNqntvpWcQ2fF73kJlgfPvjpx9u9m47LefXGLkkGuNAY1ygVcsZpfa1XPmQkWAsJ9iXfE88/DTBSH80xoHG+HxjKozexKtYLHOufchIMB5eRJw/n/FrOX4+wUh/NMaBxvh8212U4R0tk7FE1WfOKobc3qeRHzIZjHsWEd+VR93HyCXRGAca47Ntd1E2iZzQhs9VDNkwFzPBON/C//gi4xyb78rx8wlG+qMxDjTG59q+lUybyMmUMUNmNt+UMzxaGecrqd4rlYuiMQ40xmfaLotF30lmR2LDpyFDwTiXwscq43yxevkSo2CkQxrjQGN8pnKKVlUss69lnHBwxUNmgnH7CRoPz/FDeaSiMApGOqQxDjTG59m+3XXdOuUBgcwxZCoYt5XxwYcubi+kLt96IxjpkcY40BifZVxb22gWOYHlO0MOQsG4zb97t2xsN/0uv4lxIBjpj8Y40Bif5XM5w2FV9+TVb/g05CAUjNvKeGed8d3tV6tOLhjpj8Y40BifpZzgCVUlqL6MjTM86cKHTAXjTjK+/2E804+/KccbdecWjPRHYxxojM+x3Uh5WFUZq97waciNWDDuJOND35fnLCQY6Y/GONAYn2Pn4/APqSpj1ftaVjHkzmf2H9JBYzyUjLVnFoz0R2McaIzPUX7/U6rKWHXmjCM87cKHDAbjvmSs2nczEoz0R2McaIzPUX7/U6rKWPXy3TjC0y58yGQwfvv2sWisP61gpEMa40BjfI7y+59SVcaqN3yOIzztwoeMBuO33366F40fEycVjHSoWWMMa1PGwvofMhGMp1C/4fME+h8yHIyDT1/fT+n4/lPojIKRDjVrjGFtylhY/0OuJRhjH47fUv9D5oOxAcFIfzTGII0xpXpfyyn0P6RghEU0xiCNMUUwRghGWERjDNIYU6wxRghGWERjDNIYUzIfjt9Y/0MKRlhEYwzSGFM0xgjBCItojEEaY4pdqRGCERbRGIM0xhSbbyIEIyyiMQZpjCmCMUIwwiIaY5DGmGKNMUIwwiIaY5DGmGJXaoRghEU0xiCNMUVjjBCMsIjGGKQxptiVGiEYYRGNMUhjTLH5JkIwwiIaY5DGmCIYIwQj1+vF11fTX6tXNy/Ll46nMQZpjCnWGCMEI1fqxcvyd6p49bU8cCSNMUhjTLErNUIwcpVe3JS/UTtevSgPHkVjDNIYUzTGCMHINXpR/j7d85zSqDEGaYwpdqVGCEau0CN1cfKqPOEIGmOQxphi802EYOT6lC03r1+/fDH+hfrm5fYrN+MTjqExBmmMKYIxQjBydea+eGcn6hyNR3dGjTFIY0yxxhghGLk28y0a9/4ufTN9+fWxO3A0xiCNMcWu1AjByJUp+24eNsN5Q045fIrGGKQxpmiMEYKRKzP9RXrsimnpjEcuM2qMQRpjil2pEYKR61Ju6y9Hd5XOWI6eoDEGaYwpNt9ECEauy8G/R9Py43FvD6cxBmmMKYIxQjByVQ6XwunR4zamaoxBGmOKNcYIwchVma6k7v1rNFXGcnCYxhikMabYlRohGLkq01+jcvDQlJtHXUvVGIM0xhSNMUIwck2eulY6bUx9bjBqjLU0xhS7UiMEIyczf/zhq5uXz//4w4wpGPf/6U89vkNjDNIYU2y+iRCMtDJdlpzfSObF/J5rxXM//jDj7kwPLQtGjbGWxpgiGCMEI63shtAUOPecoTY+sfdGYzwTjTHFGmOEYKSV2x2ej8bi4OboTwYuv+Fp5flLWWM8D40xxa7UCMFIK9NPbPjFVNMedWwylqc/rTx/KY3xPDTGFI0xQjDSyvgDe3X7gU7zpx/ufvzhsclYnv208vylnlqD3KExBmmMKXalRghGGinla9508/Luj26bjcclY3ny08rzl3rGSTTGII0xxeabCMFII9Ny3RyL5Ys7npWM5blPK89fqCyGlqPDNMYgjTFFMEYIRhr5Wn5kg/ufCTyZPxm4HB5Unvq08vyFprA+bresxhikMaZYY4wQjDRyu+Vm3xvNPO/zD0+gFMbjru5qjEEaY4pdqRGCkUbmS6UH3oCtBFE3P9RpnOMKo8aYpDGmaIwRgpFGyk/s4Ic4TeHZS2UsHffIjbIaY5DGmGJXaoRgpJHyEzuUi/OTysGZlf56ZGHUGJM0xhSbbyIEI23MW2sO/8SmknZsFjVVcvFwkO/QGIM0xhTBGCEYaePI/jU+6egwaulmHOX4v2EaY5DGmGKNMUIw0kYJxnK017TKWA7OqewVOnKBcaAxBmmMKXalRghG2jjyIun0tHJwRqUvPqO77gTjz/6sY3++mfB/Lwe9+tfdD/nPNxNOPv+Tjn3cTPif/ms56lT/Q/5+M+Hk0+9+/N3/Mfzzu78Z/vvdv+3g3x83/w7//EWZcCAY12JKvCcL2LQUeXxPa6SsiD7nmu68iApwXn9UXpXo3ZHXSKcrrufefVOu+z7rf3cJRqAPgnEtxh/XEYF37PNamnPxWcVVMAJ9EIxrMf64jmhg4/POG4xL+qJgBHohGFdiCptycMj4vCeDcXzWMcrzn2NRX7y7+eaP/89u//njn20m/Ne7X+rvnxUM+WebCSf/5ruOjRsyPpaDXvU/5D/ZTDj59O7Wj29//P7s/23/+V2ZcGDzzUocG4zT884ajGVD6nNb66ruY/yzctCr/od0u0aQt4SLcLvG+oybUo/Y5DldkHyyrI3POkZ5/jOUGxif/SYDq7qP8Z+Xg171P6Qb/IO8802EYFyfY4NxaoxP/ljHZx2jPP94z7+BsdAYgzTGlFU0RsEYIRjXZ+xhR6TNkTf4j886Rnn+0Zb2xZU1Ru98U0tjDBKMEYJxfcaf1hFxMz7vfMG4uC9qjFEaY8oqGqP3So0QjOsz/rSezpsj9960srwvWmOMssaYsorG6L1SIwTj6pRbIMrRftOV1HP9VCv6osYYpTGmaIwRgpEWyt3v5Wi/I5/WxpTKS/94a4xB1hhTNMYIwUgLpTE+dY30rFdSy4xL/06tqjH2Hoz9D6kxBmmMEYJxdUoZe+oi5fSs83y2xpyLS/90jTFIY0xZRWN0g3+EYFyd465STmt85ymMlX1RY4zSGFNW0RjdrhEhGFenbPc8XBmPXYhsobYvaoxRGmPKKhqjYIwQjKtTflwHg6dk01l+pNV9UWOM0hhTrDFGCEZaKD+u169vyhceKtm07F6JSvV9UWOM0hhT7EqNEIw0MAfPYF/w9ZCLVX+bNMYgjTFFY4wQjDQwrR6+HFcaH++MR25bbWP6syt3/WiMQRpjisYYIRhp4Ov4w3q593aMbaM8y8+z6g1vtjTGII0xRWOMEIw0ML/TW9l3+upr+frkxXbPas0a32I1b5C6Q2MM0hhTVtEY3ccYIRjXZsqeIfbmavjq1csxBF+8+Drf4lgfTcuUvnjQMRdZNcYgjTFlFY3R7RoRgnFtph/W5lelMz7mPDf2z231oGcGo8ZYS2NMWUVjFIwRgnFtph/W+MvtddN7Xp3nR3lMX9QYT05jTLHGGCEYyZtq4nyl9NFkPNMP8qi+qDGenMaYYldqhGAkb1pZvE2X22XFyctz/RiP64sa48lpjCkaY4RgJG8Kwt10+eblq6msvXp5tlQM0xiDNMYUjTFCMJI3361x0TTGII0xRWOMEIzkTcF4lpsUT0djDNIYU1bRGN3HGCEYV2a6aloOLpXGGKQxpqyiMbpdI0Iwrsz0syoHl0pjDNIYU1bRGAVjhGBcmfFHdZ73tTkdjTFIY0yxxhghGImb7ta4omDUGGtpjCl2pUYIRuLu38Z4mTTGII0xRWOMEIzEPbyN8RJpjEEaY4rGGCEYibuK2xg1xiSNMUVjjBCMxE13a1z4bYwaY5LGmLKKxug+xgjBuC7Tj6ocXCyNMUhjTFlFY3S7RoRgXJfpR1UOLpbGGKQxpqyiMQrGCMG4Ktdxt4bGmKQxplhjjBCMpE2fxnjpm1I1xiSNMcWu1AjBSNrX8Sd1TcGoMdbSGFM0xgjBCItojEEaY4rGGCEYYRGNMUhjTNEYIwQjLKIxBmmMKatojO5jjBCM9EdjDNIYU1bRGN2uESEY6Y/GGKQxpqyiMQrGCMFIfzTGII0xxRpjhGCERTTGII0xxa7UCMEIi2iMQRpjisYYIRhhEY0xSGNM0RgjBCMsojEGaYwpGmOEYIRFGjbGP9+c9M/LQa1WZezKhswH488/v3kznu/Nmy+fy9fqhTPnuy83H8Yhbz58+Vi+Vi98H+NPv7yZhnzz5vOX8rVK8WB89/br+/F0799/LF+qJhjpT7vGONan1z8rR7UalbFrGzIcjD/9Uk42exPKxmRj/O7+kB9C2Zi8XePhkIlsDAfj25tyssn7r+XrdQQj/WnXGH8ynrTzMnZtQ0aDcUyv+978vDxaJdcYxzPd9+a78miVXDA+OuSH+u9kNBjvxeJGpDYKRvrTrDGOl/96L2NXN2QyGKfrfg+9KY/XiDXGhy/mkw/l8Rqx9N73naweMhmMP5QT3fW+PFpBMNKfVo1xuvzXeRm7viGDwbjv1TySjKnMmZY/H3NTnlEhld77h6z9TgaDcVpafER14gpG+tOoMc6v5l2XsSscMheM+1/NE8kYypx9fXGjfshQeu//XxjVQ+aCcW8u1iejYKQ/bRrj9tW85zJ2jUPGgnHn1fzzd+Opvnwux4Pq0Mlkzk54PzJkdWfMpPfhIeuupsaC8W05y+DjeKZPt0l5Mz5jOcFIf5o0xttX847L2FUOmQrG2z2Uu6e5fUGvTcZI5nwcR9loM2QkvZt+J1PB+K6c5M5pttFYuc4oGOlPi8a482rebxm7ziFDwTjm1sb9QrOtP+V4qUTmjJtGN1oNmbiPse13MhWMcwbejcBtjfy+fGEZwUh/GjTGsoty0msZu9IhQ8E4d7GHdWZ+Pa9sY4nG+PSQlbs+E7drtB0yFIzbBCzHs6/ly3WVUTDSn3hj/NN/V0436bOMXe2QoWAsp3ikzWwbUDleKNEYxzEG5XBHaMhEMI5jDMrhjsSQoWCc9zA9OMfcJMvhMoKR/qQb4+7Fv40uy9j1DpkJxnld7LFTzI/VvQVOoDHOXezQkHXvLhNI78bfyUwwfl9O8fCNbuYqWfUWOIKR/mQb472SM+iwjF3zkJlgnLeklsO7ymN111IDmTNfiiyHd5XH6q6lBtK78XcyE4zzFdNHTlEqY9W1VMFIf6KNcWdNbHobsx7L2FUPmQnGcobHq8zB1/pjBTJnHKLpkIH0HodoN2QmGMsZHrtgKhi5TMHGuLtV5Cf/uPyitzJ25UNGgnHe7vn4e3nOdxqUw2XqM+cEQ9and+sho8H42PXST+WxcriIYKQ/uca4+2o+ZGz5VWdl7NqHjATjvPhVDu85/GJ/pPrMOW7IqjcTr0/v1t/JTDCWWvjYGeaF3HK4iGCkP7nG+LNymqHkbM5Uft1ZGbv2ISPBeLjJRIKxPnNOMGT9fYyth8wE47ffvv366f2j4acxcpkaNMap2tw5qJcuY1c6ZGaN8cvnL2/evGmZOYE1xqOGrPgmlJOcYMjzB+Ne1hi5TPHGOJacwXTUaRm72iEzwXjQ4cuDRwrsazkoMmQgGA+qH7J5MJaTu12DCxNujPOLeddl7IqHPEEwHrxN4liBxnhQZMjW6V0/ZOtgnD+jserkgpH+RBvj7Yt5x2XsqodsH4zz9b+z38d4yDzk2e9jPCQwZONg3PdWcc8jGOlPsDH+7E6ylpP2VsaufMj2wThf/zv7O98cMg959ne+OSTwnWwcjPNbxVVdSRWMdCjXGO8pJ+2sjN0zjnhNQ7YPxnL2ur03rTNnnHBQN2Tj9B4nHFQM2TQYt32x7j3EBSMdyjXGe8pJOytj94wjXtOQzYNxrjl1q3eNMyc0ZNv0TgzZMBjfzXWx+tSCkf5ojAONMWXMio26LtY2c7ZDVt3en/k8xr0i38lGwfju7bzrZqP2zIKR/miMA40xZd5IWbf1pnFjTA3Z9HaNyJAtgrGcb6v6xIKR/miMA40xZH7b69rC2LQxboes/Q60DMbMd7J9MNbtuxkJRvqjMQ40xoztslPdltRBw8aYG7JheoeGbBCM78r5JpX7bkaCkf5ojAONMWK+8a76GmXLzAkO2S69U0M2CMbtXtTi/bvywGKCkf5ojAONMWEMs1H9qZtlTnLIZukdG7J5Y9yobY2Ckf5ojAONMWDbcmo3e260ypzokK12peaGbBCM88f576gsjYKR/miMA42xXrKKNWuM2SHHAOt6yAbB+JtPb8dTfS0frDGqOrlgpD8a40BjrDbfXRCpYq0a43arZ2bINsGYHLJBMO6YP4zx9eub8pVFBCP90RgHGmOt27dByZy2yVXK9JBN0js6ZNtg/PbtdtiadUbBSH80xoHGWCncF9uUsfiQLa73ZodsHIzbDyp+/bpimVEw0h+NcaAx1rm9+pc6aYNgTPfFJo0x/J1sHozb2/0rKqNgpD8a40BjrDHmwyRUxRpkTosh49d740O2D8btOmM5XkAw0h+NcaAxVth5NY9lbfwqZZMh07U2P2T7YNxeTF3+3nCCkf5ojAONcbntpyMFq1jJiFzmtBkyHIwNhjxBMM6V8Tfl+PkEI/3RGAca42K3i2L1b7G2I9sYb5cXo0Nm07vFd/IEwTivMi5fZBSM9EdjHGiMS91uooxGTjZzWg0ZTe8m/wtDMMIiGuNAY1yoURXL7mtpNmQyvdsMeYpgLIuMgpFLojEONMZFxmCYhBYst3LLdw2HzKV3qyFPGIzLt6UKRvqjMQ40xiUaRk4uGK97yEwwvnv3w6eb1z+Uo/vK+TVGLonGONAYFxhjYRLc6VmMURHInKZDpoKx3ZCRYCwn2Jd8Tzz8NMFIfzTGgcb4fLct5028isX2tbQdMpTeDYeMBOPhRcT5g4s/lePnE4z0R2McaIzPt93q+aF8ISqUOW2HDKV3wyGTwbhnEXEOxn1XWp8mGOmPxjjQGJ9te3dBeKdnkQnG7VbPnods+Z2MBON8C//jnXD+Hi8/v2CkPxrjQGN8ru27tLSJnEwZaz1kZFdq0yEzm2/KGR6tjHNh9F6pXBSNcaAxPtPYlTYaRU6kjDUfMrH5pu2QmWA89Haoc2Fc/lapgpEOaYwDjfGZyinaLN1tJBrjOOGg2ZCJYBwnHLQZMhOM20/QePihiz+URyoKo2CkQxrjQGN8nu3lv7p1ygMCjfHjOOGg5yEbfyczwbithQ82pr4rD1TsSRWM9EhjHGiMz7K97y5/a+CsfvnuBEPW19rthdRGQ4aCcbuQeFO+UMzXWCtuYhwIRvqjMQ40xmfZ1pyDql7r669SbgvjQVVD1jfG1t/JUDDeJuDuXRk/3n616uSCkf5ojAON8VnKCZ5w5mAcZ3hS1ZD1tXac4UnnD8adZHz/9cfNF95uVxcHdecWjPRHYxxojM+xvf53WNWqWXUZO8WQ1endfMhYMO4k40OVpxaM9EdjHGiMz/G5nOAJVWWsevnuFENWB2PzIXPBeCAZvy/PWEow0h+NcaAxPkf5/U85b2McR3jahQ8ZDMZ9yVi172YkGOmPxjjQGJ+j/P6nnHf5bhzhaeetteMIT+uiMX777dvHorH+tIKRDmmMA43xOcrvf0pVGau+SjmO8DSN8Tk+3YvGT4mTCkY61KwxhrUpY2H9D5kIxlOo35V6ArlP8G8lHIyDr5/eT+n4/mPojIKRDjVrjGFtylhY/0OuJRiry9gp9J/e+WBsQDDSH40xSGNMiXy6RmuCMUIw0h+NMUhjTFlFY+x/SMEIi2iMQRpjSv/Ld4P+a61ghEU0xiCNMWUVm280xgjBSH80xiCNMcWu1AjBCItojEEaY4pdqRGCERbRGIM0xhS7UiMEIyyiMQZpjCl2pUYIRlhEYwzSGFPsSo0QjLCIxhikMabYlRohGGERjTFIY0yxKzVCMMIiGmOQxphiV2qEYIRFNMYgjTHFrtQIwQiLaIxBGmOKXakRghEW0RiDNMYUu1IjBCMsojEGaYwpdqVGCEZYRGMM0hhT7EqNEIywiMYYpDGm2JUaIRhhEY0xSGNMsSs1QjBeotdfyy9oR2MM0hhT7EqNEIwX6Ob1y/Ir2tEYgzTGFLtSIwTj5Xn5WjCegMYYpDGm2JUaIRgvzovh2yQY29MYgzTGFLtSIwTjxXk1fJsEY3saY5DGmGJXaoRgvDQ3m2+TYGxPYwzSGFPsSo0QjBfmm/HbJBjb0xiDNMYUu1IjBOOFmb5NgrE9jTFIY0yxKzVCMF6WzQLjQDC2pzEGaYwpdqVGCMaL8rV8mwRjexpjkMaYYldqhGC8JNMC40AwtqcxBmmMKXalRgjGSzLuSN0QjO1pjEEaY4pdqRGC8YKUBcaBYGxPYwzSGFPsSo0QjJdj85Y3hWBsT2MM0hhT7EqNEIyX47YwCsYT0BiDNMYUu1IjBOPF2C4wDgRjexpjkMaYYldqhGDc7+XN2MFefX1RvrDr5depoL189NEz2O5I3RCM7WmMQRpjil2pEYJxx9i5Xk2/Lrk3eXU//Ob7BUcPHj2LMsvL8f8TjO1pjEEaY4pdqRGCcceYhTebX73YjcWN8auz+w/effQ8ykybD2McCMb2NMYgjTHFrtQIwbhj/BM2mbK7XDfb1sIXjzxaauZT7hTNQ54dbOXM3wjGk9EYgzTGFLtSIwTjjvFPGDLlQSMclSdNyXPfcZ3x8d/7iK/lNxyrLDAO+SwYT0VjDNIYU+xKjRCMt6b7AL+Zbwd89XLzh33zYk6zqRXOdfHVmD3fbK+5HtUZjw7G5wbbNNVmBsF4KhpjkMaYYldqhGC8NWVK6Ys70fJNCb/NxdTyyz2PPqVVMJYRNt8cwXgqGmOQxphiV2qEYLw1ZcoYMvf6Xwme7VPKl4vto09qFIyl446/STCeisYYpDGm2JUaIRhvlYAbPPhTpodeTAn0IHXGrx5TGRsF4/R7prgWjKeiMQZpjCl2pUYIxlvbYHz4h0ybW16Nz9j36NnCqCx7TgeC8VR2gvGX/+wn/fr3mwn/fTnoVf9D/nIz4eTmzeDD5t/+/nnzl5sJ/1vHEw5j7QzZ44wbO7ce/O2r953aGbJlMJY/4tHqd9v1Hsuc6ZFycGpltPKdEYyncuethgDO5o/Kq1IL5Y94dH/p9pMrHn10SqNjtt/klcnmuQTjqQhGoA8Ng3HOvnJ41/ZVsBzfNT16nmC8t/VHMJ6KYAT60DAYyyXJPZkyPbjn0SlTzxKM27e8KQTjqQhGoA/tg7Ec3Tc9uOfR8wVjeXW+vcArGE9FMAJ9aBiMt+8e85jxwX2Pni8YHwwtGE9FMAJ9aBiM02LdvkgZH9z36PQaeYZgLAuMO3+yYDwVwQj0oXkwloP7ys6ccnTftNBXDk6oTLUbg4LxVAQj0IeGwTief9+V1OlVcN+jUxqVgxMa/9i7UwnGUxGMQB/OFYxTJ9yXN2cKxjtveVMIxlPZeeebV//pP/7Hbv8dr4Tc3Ptib//2P+Q/3Ew4efWf/0G//4zfyfc7X+jxn/6H/EebCSev/mOv/kOZcNDunW+mAnA4+vb96ePPeV+mNjPNdG8owXgqO8Hoc4hqeVfplHebCX9XDnrV/5A7b0P6V3/yq1/9qsv//u8y4aBdME6d8GD07e2ExwZjuenwaccEW1lgvPfHCsZTEYxBgjHl7WbCf1sOetX/kLvBOGRQn04SjFOilIMHDgfj+OARYVQ63tOO+QT/aaT7cSwYT0UwBgnGFI0xQjDODre+w49uHowG4xHnKguM978hgvFUBGOQYEzRGCN2gvHXUwp1qINgHP/wfY9Oy5NH3MYYDMayJ/LBEwXjqawlGH1AX8SqGmPvwagxRpwkGMfTL+uEh5cndwSDcXrew3kF46msJRhX8ZHu/Q+5qsbY+6VUa4wRpwvGfYFyzJbVcnBILhinBcan3JRn04DGGKQxpmiMEYKxmPZ47gukw53w6GCMOXJ7694CTD2NMUhjTNEYIwRjMYXbvnXCw9F3eHmygWPfdEUwNqQxBmmMKT9uJtQYawnG4ojo6ycYy47UJwnGhjTGII0xRWOMEIxFTfSNv/WE+12OXqoUjA1pjEEaY8r3mwl7b4xjrdUYa50sGA9HXzfBeMgUmp0Mc9HWEox/tZmw92Dsf0iNMUhjjOgkGPelzdG3MZ6CYDyVtQSjG/wjrDEGjbVWY6x1imAcz74vT47ZsloOzk0wnopgDBKMKRpjhGAsxrPvy5MpbZZtWT0xwXgq1hiDrDGmrKIxWmOMOEEwTp1w39kPR9/JN6UeIhhPZS3BaFdqhMYYpDFGnCAYj4g+wcgujTFIY0z5L5sJNcZagnFS0wnH3yoYr43GGKQxpvxiM6HGWEswTmo64fhbe4kiwXgqGmOQxpgyBmPvjXGstRpjrRME4/heMsui7/CW1VMTjKeylmB0H2OENcag/tNbME7Gky8LximKOrmNUTCezFqC0e0aEWsJxlXsSu3/eq9gnIwnP9wJ9/3RUxSVg7MTjKciGIMEY4pdqRGCcXTM5y2u4jZGwXgy1hiDrDGmuI8xQjCOaj5vsau7NQTjyawlGO1KjdAYgzTGiPbBeET0CUbu0BiDNMYUjTFCMMIiGmOQxpiiMUYIRlhEYwzSGFM0xgjBCIusJRjdxxihMQZpjBGCkf6sJRjdrhGhMQZpjBGCkf4IxiDBmKIxRghGWMQaY5A1xhSNMUIwwiJrCUa7UiM0xiCNMUIw0h+NMUhjTNEYIwQjLKIxBmmMKRpjhGCERTTGII0xRWOMEIywyFqC0X2MERpjkMYYIRjpz1qC0e0aERpjkMYYIRjpj2AMEowpGmOEYIRFrDEGWWNM0RgjBCMsspZgtCs1QmMM0hgjBCP90RiDNMYUjTFCMMIiGmOQxpiiMUYIRlhEYwzSGFM0xgjBCIusJRjdxxihMQZpjBGCkf6sJRjdrhGhMQZpjBGCkf4IxiDBmKIxRghGWMQaY5A1xhSNMUIwwiL5YPzuy82H8Xw3H758LF+rF97wuYohf/75zZtxyDdvvnwuX6sUD8Yf3/5wM57u5v0P5UsJ2cbYcshYer999+n9OOT7978pX6rWNBin/wP6QzlaTjDSn3AwfvelnGz2IRQ7yTK2iiF/fn/IN4lsDAfjj9NL+ewmFjvJxth2yFB6v7s75PtP5et1Wgbjr6fzfiiHywlG+hMNxjEY7nvzXXm0Sq6MrWLIcbXyvjc/L48uFw3G7+++mG+kGlmuMbYeMpLebxsN2TAYSy4KRi5SMhiny1UPfSiP14iVsVUMOV1BfehNeXyxZDD+UE501/vyaJ1YY2w+ZCK9mw3ZMBj/UM4rGLlEwWDc92r++vVNeUaF1C2CKx+yNhmDwfiw5BQ/lifUSDXG9kMG0nvvkO/KExZrF4xzYRSMXKRcMO6rYhvVTSd1J8Qqhtyfi9VD5oLxwHcysHoZaownGLI+vffmYv2QzYJxm4uCkYsUC8adV/PP342n+vK5HA+q61gmcy5gyLpkjAXj2JYmH8czlS2VGzfjM6pkGuMphqxO75ZDNgvGaUfqhmDkEqWC8WM5yd3T3L6g19axyPLdKoa83Y3aYMhUMI5labRzmu0Lev3qWKQxnmTI2vR+Nw6z0WDIVsH423LSgWDkEoWCcbuL8v4elm39KcdLJTZ8rmLI8RwbTYZMBeP8yn33hXtbfqoXxyKN8SRD1qZ30yEbBePthVTByGUKBePcxR7Wmfn1vHLXZ6KMXciQNZUxFIzbmlOOZ1/Ll6vbWKIxnmbIyvRuO2SjYCynHAlGLlEoGMspHmkz2wZUjhdKlLFxjEE53HE1Q4aCca45D84xP1AOF0s0xtMMWZnebYdsE4y3C4wDwcglygTjXHMeO8W8ZvalHC8TKGOrGHIe5NCQFW+BkwnGueZ8Lce35kuADx95nkBjPNGQden9/TjIoSGr3gKnSTDuXkgVjFymTDDOV/nK4V3lsbrLlIFbBC9myIprqZlg/LT/FKXo1F6mDDTGEw1Zl95PD1m1MbVJMJYTlt4oGLlEmWAsZ3i8ysxXXsrhMoE7IcYhrnzITDCWMzx2mS+UOYHGOM4xKIe7kkPWpfc4x6Ac7koM2SIYy1ve/JVg5IJFgnHe7vn4e3nOdxqUw2XqM8eQg2gwPnYpcq5A5XCpQGMcx2g/ZCS9Ww3ZIBjLhdTfziuNgpFLFAnGefGrHN4zv9hXvU93/fLdBQ25/M3EM8FY3lDmsTPMC7nlcKlAYzzRkHXpXWphqyHzwVhy8cOfCEYuWSQYDzeZ+pfzQf2GT0MOMsE4lKWvn94/+pLdT2M80ZB16d14yHwwljj89fZXgpFLlFlj/PL5y5s3b554Oa84f2bD58UMef5g3KufNcYD+lljPKDLNcbyljdfhl8KRi5YJhgPOnx58EiJWwQPuZIhmwdjOXntnRCRxrjXOGJoyGbpPY7Y2e0a84XUza8FIxfsBMF48A6EYwXK2EFXMmTrYJw/WbD25E0bY3TIVukdGTIdjOVcv978WjBywdoH43z97+y3CB6yqiHPfh/jXmOH2ijHi7VsjNkhG6V3ZshwMJYLqWMuCkYuWftgnK//1b2pzBgK7TJnVUOe/Z1v9po//7D2ImXTxpgdslF6Z4bMBuPuhVTByEVrH4zl7HX7PVtnzjjh4NKHbBqM25ZTu6ulZWNMD9kkvVNDZoOxnKkcCUYuWPNgnGtO3epd4+W7qxmyYTC+m0tO4tStGmN+yAbpnRsyGowlCb+7eygYuUStg3HMio2qO+cbb/hc15A1rbZRML57O+8V2QicuUVjbDNkOL2zQyaD8e6FVMHIRWsdjPNGypoPEdxoWsauZ8gWwVjOt5U4cbwxjpPtiA2ZTO9xsh3VQwaDseTifCFVMHLRGgdj+b+e+pO33PC5riGrlkHbB2P1lpZRvDGOs20Fh2yX3oEhg8FY/v5NO1I3BCMXrG0wbhdLKjZSThrua7mmIRsE4/Yz6Ef1W1pG6cbYbshgeueHzAVjKYybt7wpBCMXrGkwjkGxUXuNsmXmXNWQDYJxu4OyeP+uPFAj3RjbDRlM7/yQsWB8cCFVMHLRWgbjuOQ2qj91s+W76xqyeWPcCHSdto1xIzVks8a4UTtkLBjnD2EshxuCkQvWMBi3Lad2s+dGqw2fVzZkg2D8Ws634/2P5bHF0o2x3ZDB9H5syLrSmArGv57O8dtyOBKMXLB2wZisYs3K2LUN2SAYf/Pp7Xiqr+XjIEa1J083xnZDBtM7P2QoGOc7Nf6kHI8EIxesWTCW/7sZBKpYqw2fqxhyvk8jMWSDYNwxf4Tg69c35StLpRvjjvCQwfTeERoyFIzlFLc7UjcEIxesVTDevnlH5rRN9rVc35Btg/Hbt9thK1fH0o1xV3bINukdGjITjGWBcWdH6oZg5II1CsZkyxm1yJwrHLJxMG4/Xvf167rFsYaNcZAcslF6Z4aMBOP9t7wpBCMXrE0wpqtYk+W7VQx5e7E3MmTzYNzepF7Xxlo2xsE44SAwZLP0TgwZCcZygrsXUgUjF61FMI75MAlVsQYbPq9zyPbBuF0dK8fLtG2MySHbpXdgyEQwlgC8n4uCkUvWIBh3Xs1jWRsvY1c6ZPtg3F4CrHpHs8aNMThku/QODBkIxj0XUgUjFy0fjNtPRwpWsfiGz2sd8gTBOBed35TjRRo3xuCQDdO7fsj6YJzf8uZBYRSMXLJ4MN6u3NW/xdqO7L6WVQxZXnkGuSFPEIzz2ljV+l3rxpgbsmF61w9ZH4y/Lb/9ScsDUjDSn3Qw3m6ijEZONnOud8i1BGPrxpgbsuv0rg/G8p43T/tD+Q3PJxjpTzgYG1Wx7PLdFQ95imAsS2P1Zaxl5sSGbJne1UNqjLBINBjzm0W2chs+r3rIEwZj1Y7P5o0xNuQJ0nv5kPXB+Ln89icJRi5JMhgbRk6ujF33kJlg/PH7Hz6+f/1DObqvnP/cjfFEQ9al97t3P3y6aTekxgiLBINxXGGbBHd6FqkNn1c+ZCQYywn2vV4/8fBR6hvjOMMJhqxK73GGhkNqjLBILhhvW86beBWL7Wu59iEjwViWP/e8Xo9xMfhUjhepb4ynGrIqvQ8vItYPGWiMH/Yp531dDh/ez3EswUh/csG43UX5oXwhKpQ51z5kJBgPL33NL+f7rg8eZTxJInOaD1mV3q2HrA/GX20+aurR/0oy/mH+wmKCkf7EgnG7izK807MYW1R15qxiyO3/Fm8wZCQY5xvPH39Hlvl7XLGGmWiMpxqyKr3nIR/vhPVDBoJxLzf4c8FSwbh9l5Y2kZPZ8GnIzOabcoZHi85cc5Zvpdyob4ynGrJuh9A4xKAc3hEYUjDCIqFg3C6LNYqcSBkzZCgY5yuAH8vxrrnmVL0LaaAxnmrIuvRuPKRghEVCwVhO0WbpbiOx4XOccHDFQ2aCcb4C+MhHBf5QHqnrYonGeKIh69K78ZCCERbJBOPHcorKdcoDAvtaVjHk9kJqmyEzwbgtOg+2U74rD9Rt94w0xhMNWZfe21rYZkjBCItEgnEMhI38rYGz+swx5CAUjNvlr5vyhWLOon03IBwt0BhPNGRlercdUjDCIpFg3Haxg6pe6+uX71Yx5M4nTR2wfMhQMN6+bu/eS/Dj7VerTj5INMbTDFmZ3m2HFIywSCQYywmeUJU59Rs+xxmedOFDpoJx5/X8/W9+3Hzh7XZNbFAbOZHGeJoha9N7d8iv6SEFIyySCMbtRsrDqlbNqsuYITdiwbjzev5QdeRkGuNJhqxO75ZDCkZYJBGMR76fYlUZq97waciNXDAeeD3/vjyjQqYxnmLI+vRuOKRghEUSwVh+/1Oqylj1vpZxhKdd+JDBYNz3el67pWUUaownGDKQ3u2GFIywyAmDsaqMnSpzLnzIZDB++/axF/T6026kGmP7IRPp3WxIwQiLrKUxVi/fjSM87cKHjAbjt99+uveC/imTOMHGOGg7ZCS9Ww0pGGGRRDCeQu7D8Rvqf8hwMA6+fno/vaa//5gKnEGuMY4aDhlK70GDIVsG40bVp2rMBCP9WUswVpexU+h/yHwwtpFsjM0kG2MbrYMxQjDSn7UEY+rD8Zvqf8i1BGO4MbYRbowNCEZYZC3BWL2v5RT6H1JjDNIYIwQj/RGMQYIxRWOMEIywiDXGIGuMKRpjhGCERdYSjHalRmiMQRpjhGCkPxpjkMaYojFGCEZYZC3BaFdqhMYYpDFGCEb6Y/NNkM03KRpjhGCERQRjkGBM0RgjBCMsYo0xyBpjisYYIRhhkbUEo12pERpjkMYYIRjpj8YYpDGmaIwRghEWWUsw2pUaoTEGaYwRgpH+2HwTZPNNisYYIRhhEcEYJBhTNMYIwQiLWGMMssaYojFGCEZYZC3BaFdqhMYYpDFGCEb6ozEGaYwpGmOEYIRF1hKMdqVGaIxBGmOEYKQ/Nt8E2XyTojFGCEZYRDAGCcYUjTFCMMIi1hiDrDGmaIwRghEWWUsw2pUaoTEGaYwRgpH+aIxBGmOKxhghGGGRtQSjXakRGmOQxhghGOmPzTdBNt+kaIwRghEWEYxBgjFFY4wQjLCINcYga4wpGmOEYIRF1hKMdqVGaIxBGmOEYKQ/GmOQxpiiMUYIRlhkLcFoV2qExhikMUYIRvpj802QzTcpGmOEYIRFBGOQYEzRGCMEIyxijTHIGmOKxhghGGGRtQSjXakRGmOQxhghGOmPxhikMaZojBGCERZZSzDalRqhMQZpjBGCkf7YfBNk802KxhghGGERwRgkGFM0xgjBCItYYwyyxpiiMUYIRlhkLcFoV2qExhikMUYIRvqjMQZpjCkaY4RghEXWEox2pUZojEEaY4RgpD823wTZfJOiMUYIRlhEMAYJxhSNMUIwwiLWGIOsMaZojBGCERbZCcaPf/Mvfv83/+Zf/L7Lf3+7mfA//f5v+p1w+Lf3If/m93+xmXDyw+/+3+GfT/3986/+1V/87ofNhP/5d+OI/9fwz87DffyzO+Rmyp5sZvv0u+H/+d2nzYST3/4/vfpcJhwIRjrxTfkrCXBef1ReleDMBCPQB8FIJwQj0AfBSCcEI9AHwUgndjbf/M1PO/b7zYRfykGv+h9y3Dc7+d3bjv1uM+HHctCr/occ981Ofv9XvfqvZcKBzTd0wn2MQe5jTOn/FsHBqu5j/PWf/OpXv+ryP7dr0B/BGCQYU/q/RXDgPsYIwUh/1hKMPl0jQmMMWldjLDHUH8FIf9YSjN75JmItwfj9ZsLeG2P/Q2qMsIjGGKQxpmiMEYIRFtEYgzTGFGuMEYIRFtEYgzTGFI0xQjDCIhpjkMaY8l82E/beGPsfUjDCImsJRp/gH7GWYPzFZsLeG2P/QwpGWGQtweg+xohVBWPvjbH/IQUjLCIYgwRjijXGCMEIi1hjDLLGmGJXaoRghEXWEox2pUZojEEaY4RgpD8aY5DGmKIxRghGWERjDNIYUzTGCMEIi2iMQRpjisYYIRhhkbUEo/sYIzTGII0xQjDSn7UEo9s1IjTGII0xQjDSH8EYJBhTNMYIwQiLWGMMssaYojFGCEZYZC3BaFdqhMYYpDFGCEb6ozEGaYwpGmOEYIRFNMYgjTFFY4wQjLCIxhikMaZojBGCERZZSzC6jzFCYwzSGCMEI/1ZSzC6XSNCYwzSGCMEI/0RjEGCMUVjjBCMsIg1xiBrjCkaY4RghEXWEox2pUZojEEaY4RgpD8aY5DGmKIxRghGWERjDNIYUzTGCMEIi2iMQRpjisYYIRhhkbUEo/sYIzTGII0xQjDSn7UEo9s1IjTGII0xQjDSH8EYJBhTNMYIwQiLWGMMssaYojFGCEZYZC3BaFdqhMYYpDFGCEb6ozEGaYwpGmOEYIRFNMYgjTFFY4wQjLCIxhikMaZojBGCERZZSzC6jzFCYwzSGCMEI/1ZSzC6XSNCYwzSGCMEI/0RjEGCMUVjjBCMsIg1xiBrjCkaY4RghEXWEox2pUZojEEaY4RgpD8aY5DGmKIxRghGWERjDNIYUzTGCMEIi2iMQRpjisYYIRhhkbUEo/sYIzTGII0xQjDSn3wwfvflw814vg83X76Ur9UL3wmxjiE/v3kzDfnmy+fytUrxYHz3ix/ej6e7ef9D+VJCtoytYsi3v/jNNOT795/Kl6o1DcY/jOf9UI6WE4z0JxyM330pJ5t9+FgeqZTMnFUM+dMHQyYCPByMb6eX8lkudpJlbJVDfi1fr9MyGH89nfcP5XA5wUh/osE4rrHd9+Hn5dEqueW79Q75pn7IaDD+OJXuXe9/Ux6rlCtjqxjy7SNDJmpjw2AsuagxcpGSwfihnOi+m/J4jdiGz1UP+aE8vlgyGL+WE911Ux6tEytjKx7yfXm0QsNgnP+KCkYuUTAYH/6v3ln163mujK1iyH25OJTG8oylgsG49zv5i/KEGqkytooh715F3VH9v13aBeNcGAUjFykXjNM+kcfV17FQGVv9kJXxnQvGva/mr1+/K0+pECpj1z5ks2Dc5qJg5CLFgnHnf5t//vl4qs8720dqm06ojK1iyJ2+GB8yFozvylkGn8Yzfb19fQ9cA8yUsVUMOcbrJD5ks2CcdqRuCEYuUSoYb1+6d3eIfC5fq69jkVsEL2LIqs6YCsbbV/Od02xf0OuX8CJl7AKGrEzGVsH41+WkA8HIJQoF49iVNu4Xmu1lwXK8VOJOCEPGgnGu3ndfuLcv8lXn3oiUMUM2CsbbC6mCkcsUCsa50DysM/PreeXiWCJzDJkKxu3rdjmezdsrq9vY+AdUljFDtgrGcsqRYOQShYKxnOKRNjNuSNkoxwsllu/GMQblcMfVDBkKxrnmPDjHfA2wHC6WKGOGbBSMtwuMA8HIJcoE47wu9tgp5gpU9+4ygQ2fFzNkxVvgZILx+3KKj+X41lyAat9dJlDGVjHkPEqjIZsE4+6FVMHIZcoE47yRshzeVR6r204ZKGOrGHK+XloO7yqPVQyZCcYf9p+iFJ3a7ZSBMmbIRsFYTlj+z0kwcokywVjO8HiVOfhaf6xAGRuHuPIhM8FYzvDYZb5Q5gTK2DjHoBzuupohWwRjCcS/EoxcsEgwjk1p7xnmq4PlcJn6MmbIQTQYH3uj60/lsXK4VKCMjWNc+ZANgrFcSP0yB6Rg5BJFgnFe/CqH98ybRr4rx4vU3yJoyEEmGEuZeewM827KcrhUoIwZskUwllwc4lAwcsEiwfixnKEc3nO4BR2p/k6IVQx5uBPWD5kJxiETPv7L94++ZP/TcvZyuFRiw+dKhvz6qd2Q+WAsO1J/LRi5aJk1xr/43z6/efOmZRkL3CK4jiG/NB0yFYx79bN8d8DVDBkPxvKWN78dfikYuWCZYDzocFU7Uv3y3WFXMmTzYCwnr/0wwUgZ22sc8RqGTAfjfCH1T4ZfC0Yu2AmCsZcNnwddyZCtg3HeMVL7sRBNy9iqhqz6MaWDsZzr15tfC0YuWPtgnBfG6t5urXEZu5YhGwfj9uMsyvFiLcvYFQ0ZDsbf7p5KMHLB2gdj4P1aBo3L2LUM2TgYy7rYo7cfPEvLMnZFQ2aD8XZH6oZg5IK1D8Zy9srTNy5j44SDSx+yaTBuW07trpaWZeyqhswGYzlTORKMXLDmwTjfgFC3epf5qMO9rmbIhsH4di45iVO3KmNXNmQ0GEsSjguMt4eCkUvUOhjndbHasyc+0Wmv6xmyUTD++Iv5TT83AmduUcbWMeTb5JDJYLx7IVUwctFaB+O8kbJuV0vjzLmeIVsEYznfVuLE8TI2TrZjFUN+X76+WDAYSy7OF1IFIxetcTCW/+t5/frn5QtLtVy+u6Ih2wdj7c2Bk3gZG2fbupYhg8FY3vLmu3IoGLlobYNx+2pet9tz0HDD5zUN2SAYx960Vf2x+JN0GbvSIXPBWArjX5fDgWDkgjUNxrFBbdR9zOHGeKommXNVQzYIxrE37bhJnDhdxlY55PvqIWPB+OBCqmDkorUMxnHJbVR/6mZl7LqGbN4YNwJdp20Z27iKIWPBWFJw3pG6IRi5YA2DcdtyEmduVcaubMgGwbi7i7J4X71rJF3GrnTIVDCWt7z5Ug5HgpEL1i4Yb1/Nqz6xomh0i+C1DdkgGD/98HY81afbu+9ev/5xfGi5dBlbx5Af00OGgvH+nRojwcgFaxaMN+WsofO2uRNiFUOWV6BB/ZANgnHH/MbX1e/YEi9jO65pyFAwllPsXkgVjFy0VsE433WXqWKNMmcVQ97mYmDItsG4854tlatj6TK264qGzARj+Ru4+RDGHYKRC9YoGMNVrM3y3RUO2TgYb9/8uvL0DcvY4GqGjATjfCF18yGMOwQjF6xNMKarWJMNn9c4ZPNg3N6kXtfGWpaxwTjh4NKHjARjOcHdC6mCkYvWIhhvN4uEz5nMnOscsn0wfi2nr/sgwbZl7GqGTARjecubB79fMHLBGgTjzqt5qorlN3xe6ZDtg3F7CfCHcrxI4zJ2LUMGgvHRHakbgpELlg/Gj+V0g1gVi+9rWcWQ28+Zyg15gmCcN1RWvdVn4zJ2LUPWB+P8ljf3L6QKRi5aPBhvF8VqP6vijmzmrGLI8sozyA15gmCc18aq7jNoXcauZMj6YCy39j/tD+U3PJ9gpD/pYLzdRFn/1qO7xquKqcy53iHXEoyty9iVDFkfjH9dfvuTljdHwUh/wsF4W8WykRPd8HnFQ54iGMvSWN9l7DqGFIywSDQYdzaLpBbFZrkydtVDnjAYq3Z8Ni9j1zHkCS+lCkYuSTIYd17Nczs9i9iGz+seMhOMv/jx0w83ezdLlvOfu4ytYsi3bYesD8bP5bc/STBySYLB2KrljFL7Wq58yEgwlhPse71+4uGj1JexcYZrH1JjhEVywTimwuhNvIrFMufah4wE4+Glr7FHDb6W40Xqy5ghB4HG+GGfct7X5fDh/RzHEoz0JxeM212U4R0tk7FE1WfOKobcvubkh0wG456lr3fl0TPfImjIQX0w/upXm7dIfey/+T7Gza+rCEb6EwvG7S7KJpET2vC5iiEb5mImGOcbzx9fGptf7N+V40Xqy5ghB4lg3Kf8NV1+/+JMMNKfVDBu30qmTeRkypghM5tvyhkeLTrz9b/lWyk3Ahs+xyEG5fCOaxnyBMG4fG1xJhjpTygYt8ti0XeS2ZHY8GnIUDDOVeaxojNfrK5avQuUMUMOBCMsEgrGcopWVSyzr2WccHDFQ2aCcfu5Dw/P8UN5pK6LJcqYIQUjLJQJxu3bXdetUx4QyBxDpoJxW3QefFTg9vJf1a6WSBkzpGCEhSLBOK6tbTSLnMDynSEHoWDcvmrfu9Fgu+l3+a13k0AZM6RghIUiwXjc22NU3ZNXv+HTkINQMG6Lzp3VsXe3X606+SBRxgwpGGGZSDCWEzyhqgTVl7Fxhidd+JCpYNx5PX//w3imH7drYoPayImUsdUO+ZtyvFE3pGCERRLBuN1IeVhVGave8GnIjVgw7ryeP/R9ec5ykTJmSMEIiySCcefj8A+pKmPV+1pWMeTOZ/Yf0kFjPPR6Xl3FUmXs6ocUjLBIIhjL739KVRmrzpxxhKdd+JDBYNz3el67pWUUKmPXPqRghEVOGIxVZax6+W4c4WkXPmQyGL99+9gLeqCJDVJl7MqHFIywyFoaY/WGz3GEp134kNFg/PbbT/de0D9mEidYxgZXPKRghEUSwXgK9Rs+T6D/IcPBOPj09f30mv7+UypwBrkyNrrWIVsG4+3nbFQSjPRnLcEY+3D8lvofMh+MbSTLWDP9D9k2GEMEI/1ZSzBW72s5hf6HXEswhstYG/0PKRhhEcEYJBhTNMYIwQiLWGMMusY1xjY0xgjBCIusJRgzH47fWP9DaoxBGmOEYKQ/GmOQxpiiMUYIRlhkLcFoV2qExhikMUYIRvpj802QzTcpGmOEYIRFBGOQYEzRGCMEIyxijTHIGmOKxhghGGGRtQSjXakRGmOQxhghGOmPxhikMaZojBGCERZZSzDalRqhMQZpjBGCkf7YfBNk802KxhghGGERwRgkGFM0xgjBCItYYwyyxpiiMUYIRlhkLcFoV2qExhikMUYIRvqjMQZpjCkaY4RghEXWEox2pUZojEEaY4RgpD823wTZfJOiMUYIRlhEMAYJxhSNMUIwwiLWGIOsMaZojBGCERZZSzDalRqhMQZpjBGCkf5ojEEaY4rGGCEYYZG1BKNdqREaY5DGGCEY6Y/NN0E236RojBGCERYRjEGCMUVjjBCMsIg1xiBrjCkaY4RghEXWEox2pUZojEEaY4RgpD8aY5DGmKIxRghGWGQtwWhXaoTGGKQxRghG+mPzTZDNNykaY4RghEUEY5BgTNEYIwQjLGKNMcgaY4rGGCEYYZG1BKNdqREaY5DGGCEY6Y/GGKQxpmiMEYIRFllLMNqVGqExBmmMEYKR/th8E2TzTYrGGCEYYRHBGCQYUzTGCMEIi1hjDLLGmKIxRghGWGQtwWhXaoTGGKQxRghG+qMxBmmMKRpjhGCERdYSjHalRmiMQRpjhGCkPzbfBNl8k6IxRghGWEQwBgnGFI0xQjDCIjvB+Hc3f3nzl3/Z6X9/t5nw728OHj7WzX9dDzn9u5lwsvlx/+3N3/7l3/5y+G/4RTf/baYav5N/d/PLccRf9jVf+e/ekMOY5XvZw3/jRMNcmwknf/f/9eq/lQkHgpFOfFP+SgKc1x+VVyU4M8EI9EEw0gnBCPRBMNIJwQj0QTDSCcEI9EEw0gnBCPRBMNIJwQj0QTDSCcEI9EEw0gnBCPRBMNKLm62vXet/wsE6vo2T8oVO9T/hYB3fxkn5Qo/KhIPymgQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAa/f19THKkwHg0r0s0XfQy/JkALh0RwVjeS4AXLxjgvFFeS4AXLxjgrE8FQCu3JSL35QjALhupU2WIwC4clMuWmEEgI2pML4qRwBw5cZcVBgBYKQwAsCtb8ZctCUVAEYKIwDcejHmohVGABgpjACcwTdfpwC6efW1fKUTCiMAJzEFTsmbkj5FV9n4ahzJx00B0NiUhTu/3PWqm4JWZrMlFYDGpiunm1/djL+659hkPO5z9wcLO5/CCMBpjMH46rG6ODkyGad8PcKyy7NlOiuMALQ29sRXt7n48psxfV5OFW1wXBgdHYzLSp/CCMCJTIlT3lXm5e4a3hyN5fCwtsFYYrscAUA7Y+JMGfjq3taWEnZHRVnbYFQYATiRUhU3HuZO6Yzl6KCmwagwAnAqt3tuHrsTopOmNk6hMAJwAtum9+gem+nRm3J0LgojACczB+OeOjY9WA7OZRrCvf0AnMATW0+nh8vBmSiMAJzOFDp769hUKM9b1sYRFEYATmJKnb11bArGs77hTLnYW44AoKVymXJvHesgGMcJFEYATuKp9bvzB6PCCMAJTcG4/w7B8wfjOIDCCMBpTMH3VDCWg3OYBnhVjgCgrSl39vexswfj+OcrjACcyFP3KZ77PsYpmM/91jsAXI0xdw5cqByD8ZgLmW0+wf+pTbMAkDXmzoGseurxranaHeFZn+BvhRGAk5oa2YFCNj4eDcbnNEaFEYDTmpJn/90Y06c1ni0Yp5M+53cAQI0pecrBI55slFstglFhBODEngrG6fFj7u9vEYzTlliFEYCTudkkz4G9LU8FZ1OlMJ71jVoBuC5j8jx1t8a5glFhBODUnoqe8fEz3S1RCmM5AoD2ntx0+tTjLSmMAJzaU5tOp8fPk00KIwAn99Sm06lRnmf3y/hHK4wAnNJTm06nN0A9y32ECiMAp/dUMJ7xbo3xT3ZvPwAn1e9tjAojAGcwhs+BZbyjP3QqbpzMvf0AnNaYPgeC8anHm1EYATiDJzedjo+fIxjHP9gKIwCnNfWyp25jPEM8TbthFUYATqvb2xjHP9cKIwAndtxtjKfPp2muM71FKwDX66nPzjjX3RrjH6swAnBqY/7096FTCiMA5zEG0FN3a5whoF5uEtmWVADYeqUwAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEClv/f3/n9kr5DkdPG3QQAAAABJRU5ErkJggg==\" alt=\"A 4-by-7 matrix with each 2-by-3 block shaded a different color. The overall dimensions (m = 4, n = 7) of the matrix are noted. The 2-by-3 (p-by-q) size of each block is also noted.\" data-image-state=\"image-loaded\" width=\"454\" height=\"254\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 363px 8px; transform-origin: 363px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou can assume m, n, p, and q are all positive integers. (They can have the value 1, however.) As in the illustration above, m may or may not be divisible by p, and n may or may not be divisible by q. It is even possible for m \u0026lt; p or n \u0026lt; q. The resulting matrix will always be m-by-n.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function A = blockjplusk2(m,n,p,q)\r\nA = m+n+p+q;\r\nend","test_suite":"    %%\r\n    A = blockjplusk2(8,8,3,1);\r\n    Asol = [1 2 3 4 5 6 7 8;1 2 3 4 5 6 7 8;1 2 3 4 5 6 7 8;2 3 4 5 6 7 8 9;2 3 4 5 6 7 8 9;2 3 4 5 6 7 8 9;3 4 5 6 7 8 9 10;3 4 5 6 7 8 9 10];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk2(2,3,4,2);\r\n    Asol = [1 1 2;1 1 2];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk2(3,7,3,3);\r\n    Asol = [1 1 1 2 2 2 3;1 1 1 2 2 2 3;1 1 1 2 2 2 3];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk2(8,3,4,4);\r\n    Asol = [1 1 1;1 1 1;1 1 1;1 1 1;2 2 2;2 2 2;2 2 2;2 2 2];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk2(4,5,1,1);\r\n    Asol = [1 2 3 4 5;2 3 4 5 6;3 4 5 6 7;4 5 6 7 8];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk2(5,7,4,1);\r\n    Asol = [1 2 3 4 5 6 7;1 2 3 4 5 6 7;1 2 3 4 5 6 7;1 2 3 4 5 6 7;2 3 4 5 6 7 8];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    A = blockjplusk2(5,4,1,2);\r\n    Asol = [1 1 2 2;2 2 3 3;3 3 4 4;4 4 5 5;5 5 6 6];\r\n    assert(isequal(A,Asol))\r\n    %%\r\n    m = randi(8);\r\n    n = randi(8);\r\n    p = randi(4);\r\n    q = randi(4);\r\n    A = blockjplusk2(m,n,p,q);\r\n    assert( isequal(size(A),[m,n]) \u0026\u0026 (A(1,1)==1) \u0026\u0026 isequal(A(end,end),ceil(n/q)+ceil(m/p)-1) )","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":287,"edited_by":287,"edited_at":"2022-10-24T13:15:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":97,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-07-28T17:39:32.000Z","updated_at":"2026-03-11T20:39:48.000Z","published_at":"2022-10-24T13:15:00.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven m, n, p, and q, create an m-by-n matrix made up of submatrices, each sized p-by-q (if possible - the last row and column of blocks may be smaller). The elements of the (j,k)th block all have the same value: (j+k-1).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if m = 4, n = 7, p = 2, and q = 3, the matrix is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"254\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"454\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"A 4-by-7 matrix with each 2-by-3 block shaded a different color. The overall dimensions (m = 4, n = 7) of the matrix are noted. The 2-by-3 (p-by-q) size of each block is also noted.\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can assume m, n, p, and q are all positive integers. (They can have the value 1, however.) As in the illustration above, m may or may not be divisible by p, and n may or may not be divisible by q. It is even possible for m \u0026lt; p or n \u0026lt; q. The resulting matrix will always be 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this tower of blocks going to fall?\r\n\r\n\u003c\u003chttp://www.alfnie.com/software/equilibrium02.jpg\u003e\u003e\r\n\r\n*Description*\r\n\r\nGiven a stacking configuration for a series of square blocks, your function should return _true_ if they are at equilibrium and _false_ otherwise. \r\n\r\nThe block configuration for N blocks is provided as a input vector *x* with N elements listing the _x-coordinates_ of the left-side of each block. The blocks are square with side equal to 1 (so the i-th block left side is at x(i) and its right side is at x(i)+1). The _y-coordinates_ of each block are determined implicitly by the order of the blocks, which are dropped \"tetris-style\" until they hit the floor or another block. \r\n\r\nAll blocks are identical (same dimensions and mass) and perfectly smooth (friction is to be disregarded).\r\n \r\nIntermediate positions may be unstable. You are only required to determine whether the final configuration is stable.\r\n\r\n*Examples*:\r\n\r\nExample (1) \r\n \r\n x = [0 0.4];\r\n\r\n\u003c\u003chttp://www.alfnie.com/software/equilibrium01a.jpg\u003e\u003e\r\n\r\nThe first block bottom-left corner is at (0,0) and the second block falls on top of it, with its bottom-left corner at (0.4 1). This configuration is stable so your function should return _true_.\r\n\r\nExample (2) \r\n\r\n x = [0 0.6];\r\n\r\n\u003c\u003chttp://www.alfnie.com/software/equilibrium01b.jpg\u003e\u003e\r\n\r\nThe first block bottom-left corner is at (0,0) and the second block falls on top of it, with its bottom-left corner at (0.6 1). This configuration is unstable (the second block will fall) so your function should return _false_.\r\n\r\nExample (3) \r\n\r\n x = [0 1.5 0.6];\r\n\r\n\u003c\u003chttp://www.alfnie.com/software/equilibrium01c.jpg\u003e\u003e\r\n\r\nThe three block bottom-left corner coordinates are (0,0) (1.5,0) and (0.6,1). This configuration is stable so your function should return _true_.\r\n\r\nExample (4) \r\n\r\n x = [0 .9 -.9 zeros(1,5)];\r\n\r\n\u003c\u003chttp://www.alfnie.com/software/equilibrium01d.jpg\u003e\u003e\r\n\r\nThis configuration is unstable, but note that if instead of five we add a few more blocks on top of this at the 0 position that will keep the tower from falling!\r\n\r\nExample (5) \r\n\r\nx = cumsum(fliplr(1./(1:8))/2);\r\n\r\n\u003c\u003chttp://www.alfnie.com/software/equilibrium01e.jpg\u003e\u003e\r\n\r\nThis configuration is stable (see the \u003chttp://en.wikipedia.org/wiki/Block-stacking_problem classic optimal stacking solution\u003e) so your function should return _true_.\r\n\r\n*Display*\r\n\r\nIf you wish, you may display any given block configuration *x* using the code below:\r\n\r\n clf;\r\n y=[];\r\n for n=1:numel(x), y(n)=max([0 y(abs(x(1:n-1)-x(n))\u003c1)+1]); end\r\n h=arrayfun(@(x,y)patch(x+[0,1,1,0],y+[0,0,1,1],rand(1,3)),x,y);\r\n text(x+.5,y+.5,arrayfun(@num2str,1:numel(x),'uni',0),...\r\n 'horizontalalignment','center');\r\n\r\nVisit \u003chttp://www.mathworks.com/matlabcentral/cody/problems/1910-block-canvas Block canvas\u003e for a related Cody problem.","description_html":"\u003cp\u003eIs this tower of blocks going to fall?\u003c/p\u003e\u003cimg src = \"http://www.alfnie.com/software/equilibrium02.jpg\"\u003e\u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven a stacking configuration for a series of square blocks, your function should return \u003ci\u003etrue\u003c/i\u003e if they are at equilibrium and \u003ci\u003efalse\u003c/i\u003e otherwise.\u003c/p\u003e\u003cp\u003eThe block configuration for N blocks is provided as a input vector \u003cb\u003ex\u003c/b\u003e with N elements listing the \u003ci\u003ex-coordinates\u003c/i\u003e of the left-side of each block. The blocks are square with side equal to 1 (so the i-th block left side is at x(i) and its right side is at x(i)+1). The \u003ci\u003ey-coordinates\u003c/i\u003e of each block are determined implicitly by the order of the blocks, which are dropped \"tetris-style\" until they hit the floor or another block.\u003c/p\u003e\u003cp\u003eAll blocks are identical (same dimensions and mass) and perfectly smooth (friction is to be disregarded).\u003c/p\u003e\u003cp\u003eIntermediate positions may be unstable. You are only required to determine whether the final configuration is stable.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExamples\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eExample (1)\u003c/p\u003e\u003cpre\u003e x = [0 0.4];\u003c/pre\u003e\u003cimg src = \"http://www.alfnie.com/software/equilibrium01a.jpg\"\u003e\u003cp\u003eThe first block bottom-left corner is at (0,0) and the second block falls on top of it, with its bottom-left corner at (0.4 1). This configuration is stable so your function should return \u003ci\u003etrue\u003c/i\u003e.\u003c/p\u003e\u003cp\u003eExample (2)\u003c/p\u003e\u003cpre\u003e x = [0 0.6];\u003c/pre\u003e\u003cimg src = \"http://www.alfnie.com/software/equilibrium01b.jpg\"\u003e\u003cp\u003eThe first block bottom-left corner is at (0,0) and the second block falls on top of it, with its bottom-left corner at (0.6 1). This configuration is unstable (the second block will fall) so your function should return \u003ci\u003efalse\u003c/i\u003e.\u003c/p\u003e\u003cp\u003eExample (3)\u003c/p\u003e\u003cpre\u003e x = [0 1.5 0.6];\u003c/pre\u003e\u003cimg src = \"http://www.alfnie.com/software/equilibrium01c.jpg\"\u003e\u003cp\u003eThe three block bottom-left corner coordinates are (0,0) (1.5,0) and (0.6,1). This configuration is stable so your function should return \u003ci\u003etrue\u003c/i\u003e.\u003c/p\u003e\u003cp\u003eExample (4)\u003c/p\u003e\u003cpre\u003e x = [0 .9 -.9 zeros(1,5)];\u003c/pre\u003e\u003cimg src = \"http://www.alfnie.com/software/equilibrium01d.jpg\"\u003e\u003cp\u003eThis configuration is unstable, but note that if instead of five we add a few more blocks on top of this at the 0 position that will keep the tower from falling!\u003c/p\u003e\u003cp\u003eExample (5)\u003c/p\u003e\u003cp\u003ex = cumsum(fliplr(1./(1:8))/2);\u003c/p\u003e\u003cimg src = \"http://www.alfnie.com/software/equilibrium01e.jpg\"\u003e\u003cp\u003eThis configuration is stable (see the \u003ca href = \"http://en.wikipedia.org/wiki/Block-stacking_problem\"\u003eclassic optimal stacking solution\u003c/a\u003e) so your function should return \u003ci\u003etrue\u003c/i\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDisplay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIf you wish, you may display any given block configuration \u003cb\u003ex\u003c/b\u003e using the code below:\u003c/p\u003e\u003cpre\u003e clf;\r\n y=[];\r\n for n=1:numel(x), y(n)=max([0 y(abs(x(1:n-1)-x(n))\u0026lt;1)+1]); end\r\n h=arrayfun(@(x,y)patch(x+[0,1,1,0],y+[0,0,1,1],rand(1,3)),x,y);\r\n text(x+.5,y+.5,arrayfun(@num2str,1:numel(x),'uni',0),...\r\n 'horizontalalignment','center');\u003c/pre\u003e\u003cp\u003eVisit \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/1910-block-canvas\"\u003eBlock canvas\u003c/a\u003e for a related Cody problem.\u003c/p\u003e","function_template":"function y = equilibrium(x)\r\n  y = true;\r\nend","test_suite":"%%\r\nx = [0 0.6];\r\nassert(isequal(equilibrium(x),false))\r\n\r\n%%\r\nx = [0 0.4];\r\nassert(isequal(equilibrium(x),true))\r\n\r\n%%\r\nx = [0 1.5 0.6];\r\nassert(isequal(equilibrium(x),true))\r\n\r\n%%\r\nx = cumsum(fliplr(1./(1:16))/2);\r\nassert(isequal(equilibrium(x),true))\r\n\r\n%%\r\nx = [1.5 2.5 1 0.25 5.5 3.5 -1.5 -0.25 -4 -1.75 6.25 -1.25 0.5 1 -0.5 4.75 -1.25 -1.5 0.5 1.5];\r\nassert(isequal(equilibrium(x),false))\r\n\r\n%%\r\nx = [-1.5 -0.25 -0.25 -1.75 1.75 -2 -5.25 2.25 0.75 -0 0.25 0 -1 -1.5 4.75 -1 4.75 -2.5 3.25 -1];\r\nassert(isequal(equilibrium(x),true))\r\n\r\n%%\r\nx = [1.25 -1.75 -0.75 2.75 -1.5 3.75 1.75 1.5 -0.25 -4 -0.5 -2 1.75 -3.5 -2 -3.75 0 -1.75 1.75 3.25 -1.5 -0.5 1.25 -2 1.5 3 -0.25 1.75 -0.5 2.75 0.5 -4.25 1.5 5.5 3 4.25 2.75 -0.75 0.5 3 3.5 3.25 1.75 1.5 3.25 2.5 5.5 -2 -3.75 -1 5 0.25 -3.75 5.5 1.75 2 1.75 0.5 -3.75 1.5 -0 2 0.5 0 -0.25 0.25 -9 1.75 -3.75 1.25 -3.75 -0 1.75 3.5 -3.75 3.75 -5.75 1.25 3.5 1.5 -2 2 -2.5 -1.5 3 1.75 -1.5 3.25 1.75 -1.5 1.25 -2.5 1.25 -4.25 3.25 -2.5 1.75 1.75 7.25 3.5];\r\nassert(isequal(equilibrium(x),true))\r\n\r\n%%\r\nx = [-0.5 1.5 3.5 0.5 -5.75 2 -1.5 0.25 -0.25 -3 -0.25 3 -3.5 -4.5 1.75 -0.25 0.75 3 0.25 -2.5 2.25 -0.25 1.75 -1.5 -5 -0 -0.5 -2 -0 4.75 -0 2 3.5 1.5 -2.25 3.5 -0.5 4.5 2.5 0.5 1.75 3.5 -0 -2.25 -0.25 -4.75 -2.5 -0.75 -6 2.75 -5 2.25 1 -2.25 -0.75 -0.25 -3.5 0.75 -0 -0.5 -0.5 -1.75 -2 -0.25 -0.25 5 -0.25 -0.75 -0.25 -5 -2 -0.25 -5.5 -5 -0.5 -2 1 -0.75 2 3.25 4.5 2.25 1.25 -0.25 -0.5 -0.25 -2.5 -5 2.25 -2 7.5 6.5 2.25 -0.25 -0.5 7.25 -2.5 1 -2.5 -4.75];\r\nassert(isequal(equilibrium(x),false))\r\n\r\n%%\r\nx = [0.1 0.1 -4.6 -0.4 -1.5 1.6 3 2.7 2.3 -2.7 0.1 -1.7 0 4.4 3.8 -0.4 -2 -0.6 3.3 2.5 -3 -1.7 3.1 2.7 2.7 3.1 -0.4 1.1 -0.2 -0.1 -0.3 2.7];\r\nassert(isequal(equilibrium(x),true))\r\n\r\n%%\r\nx = [0.1 0.1 -4.6 -0.4 -1.5 1.6 3 2.7 2.3 -2.7 0.1 -1.7 0 4.4 3.8 -0.4 -2 -0.6 3.3 2.5 -3 -1.7 3.1 2.7 2.7 3.1 -0.4 1.1 -0.2 -0.1 -0.3 2.7 -1.8 2.3];\r\nassert(isequal(equilibrium(x),false))\r\n\r\n%%\r\nx = [-3.5 2.6 -0.7 -1.1 -2.6 -2 0.8 -0.7 2.7 0.4 -5 3.7 -1.2 -1.3 2.8 0.8 -1.5 -1.8 0 -0 4.8 -1.4 -1.2 -1.5 1 0.2 2.6 1.7 1.6 -1.3 2.1 -1.5 -1.4 2 0.1 -0.1 -0.1 4.6 -3 -0.3 0.2 -1.9 -0 0.1 0 2.1 -1.7 -3.1 -0 0.2 -0.1 -0.5 4.7 -1.8 -0.1 -2.2];\r\nassert(isequal(equilibrium(x),false));\r\n\r\n%%\r\nx = [-2.5 2.6 -0.7 -1.1 -2.6 -2 0.8 -0.7 2.7 0.4 -5 3.7 -1.2 -1.3 2.8 0.8 -1.5 -1.8 0 -0 4.8 -1.4 -1.2 -1.5 1 0.2 2.6 1.7 1.6 -1.3 2.1 -1.5 -1.4 2 0.1 -0.1 -0.1 4.6 -3 -0.3 0.2 -1.9 -0 0.1 0 2.1 -1.7 -3.1 -0 0.2 -0.1 -0.5 4.7 -1.8 -0.1 -2.2];\r\nassert(isequal(equilibrium(x),true));\r\n\r\n%%\r\nx =[0 .9 -.9 zeros(1,8)];\r\nassert(isequal(equilibrium(x),true))\r\n\r\n%%\r\nx =[0 .9 -.9 zeros(1,6)];\r\nassert(isequal(equilibrium(x),false))\r\n\r\n%%\r\nx = repmat([0 .7 -.7 0],1,2);\r\nassert(isequal(equilibrium(x),false))\r\n\r\n%%\r\nx = repmat([0 .6 -.6 0],1,2);\r\nassert(isequal(equilibrium(x),true))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":43,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-10-02T08:49:59.000Z","updated_at":"2013-10-08T00:22:34.000Z","published_at":"2013-10-02T09:44:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/media/image2.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId3\",\"target\":\"/media/image3.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId4\",\"target\":\"/media/image4.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId5\",\"target\":\"/media/image5.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId6\",\"target\":\"/media/image6.JPEG\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIs this tower of blocks going to fall?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eDescription\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a stacking configuration for a series of square blocks, your function should return\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etrue\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e if they are at equilibrium and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efalse\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e otherwise.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe block configuration for N blocks is provided as a input vector\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e with N elements listing the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ex-coordinates\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of the left-side of each block. The blocks are square with side equal to 1 (so the i-th block left side is at x(i) and its right side is at x(i)+1). The\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ey-coordinates\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of each block are determined implicitly by the order of the blocks, which are dropped \\\"tetris-style\\\" until they hit the floor or another block.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAll blocks are identical (same dimensions and mass) and perfectly smooth (friction is to be disregarded).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIntermediate positions may be unstable. You are only required to determine whether the final configuration is stable.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExamples\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample (1)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x = [0 0.4];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe first block bottom-left corner is at (0,0) and the second block falls on top of it, with its bottom-left corner at (0.4 1). This configuration is stable so your function should return\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etrue\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample (2)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x = [0 0.6];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId3\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe first block bottom-left corner is at (0,0) and the second block falls on top of it, with its bottom-left corner at (0.6 1). This configuration is unstable (the second block will fall) so your function should return\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efalse\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample (3)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x = [0 1.5 0.6];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId4\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe three block bottom-left corner coordinates are (0,0) (1.5,0) and (0.6,1). This configuration is stable so your function should return\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etrue\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample (4)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x = [0 .9 -.9 zeros(1,5)];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId5\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis configuration is unstable, but note that if instead of five we add a few more blocks on top of this at the 0 position that will keep the tower from falling!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample (5)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ex = cumsum(fliplr(1./(1:8))/2);\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId6\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis configuration is stable (see the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Block-stacking_problem\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eclassic optimal stacking solution\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e) so your function should return\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etrue\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eDisplay\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf you wish, you may display any given block configuration\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e using the code below:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ clf;\\n y=[];\\n for n=1:numel(x), y(n)=max([0 y(abs(x(1:n-1)-x(n))\u003c1)+1]); end\\n h=arrayfun(@(x,y)patch(x+[0,1,1,0],y+[0,0,1,1],rand(1,3)),x,y);\\n text(x+.5,y+.5,arrayfun(@num2str,1:numel(x),'uni',0),...\\n 'horizontalalignment','center');]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVisit\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/1910-block-canvas\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eBlock canvas\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for a related Cody problem.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray 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