How to eliminate or exclude complix numbers from an array solution?

Hello everyone,

Is there any way to eliminate the complex numbers (real and imaginary parts) from a array solution. Or can I exclude the complex elements so the solution will just include the real numbers. For example, in the next array I just want the real elements ( the first four elements. Thanks for any suggestion.

sol =

                                           2.7081096834177947291918098879734
                                           2.4539692532957056897462100829847
                                           1.1392825187840759291489282043638
                                          -4.0494620185638170836265057842329
    0.88295725432536163128309307530013 - 0.1308999971886537594046901453934*i
    0.1308999971886537594046901453934*i + 0.88295725432536163128309307530013
     0.1528510148755374525528985048959*i - 2.0089069727922412635133142708447
   - 0.1528510148755374525528985048959*i - 2.0089069727922412635133142708447

[srijesh raghavan]

%please help me find the values of l and m : where func should be maximum 

syms l m;

 w1= 981*l^(2)*(2-l)^(2)+(490.5*l*(2-l-m)*(-2*l^(2)-(m^(2))+(4*l)+(4*m)-(2*l*m))/2);
% 
% 
% 
% 
 w2= (-981*l*(2-l-m)*(2*l^(2)+m^(2)+(2*l*m)-(4*l)-(4*m)/2)+(490.5*(l+m)*(2-l-m)*(-2*l^(2)-2*m^(2)+(4*l)+(4*m)-(4*l*m))/2));


func = (2*w1+w2)/(2*w1^(2)+w2^2);

eqn1 = diff(func,l)
eqn2 = diff(func,m)


% sol= solve(eqn1,eqn2,'l')
sol = solve(eqn1,eqn2)
lvalue = double(sol.l);
mvalue = double(sol.m);

[Walter Roberson]

fs = double(subs(func, {l,m}, {lvalue, mvalue}));
mask = imag(fs) == 0;
[maxvalue, maxidx] = max(fs(mask));
selected_l = lvalue(mask);
selected_m = mvalue(mask);
best_l = selected_l(maxidx);
best_m = selected_m(maxidx);

Answer 1

[Matt J]

 sol = sol(sol==real(sol));

[Abdulaziz Abutunis]

That was great thank you Matt

[Walter Roberson]

The method I would use would be

sol = sol(image(sol)==0);

Answer 2

[Jose Perez-Sanabria]

x=x(imag(y)==0);
y=y(imag(y)==0);

[Ataklti Kahsay]

Dear sirs how can i solve this error for my multiobjective problem?

Obj =

   1.0e+13 *

   2.3579 + 0.0097i   0.0106 + 0.0000i


Obj =

   1.0e+11 *

   4.0416 + 2.4312i   0.0186 + 0.0110i


Obj =

   1.0e+12 *

   1.8716 + 0.2003i   0.0085 + 0.0009i


Obj =

   1.0e+11 *

   8.6100 + 3.2635i   0.0392 + 0.0147i


Obj =

   1.0e+11 *

   3.5387 + 3.1425i   0.0164 + 0.0142i


Obj =

   1.0e+12 *

   1.2556 + 0.2470i   0.0057 + 0.0011i


Obj =

   1.0e+11 *

   3.1311 + 2.6271i   0.0145 + 0.0118i


Obj =

   1.0e+11 *

   3.3460 + 2.5507i   0.0155 + 0.0115i


Obj =

   1.0e+11 *

   4.9544 + 1.6274i   0.0227 + 0.0073i


Obj =

   1.0e+12 *

   1.8264 + 0.1878i   0.0083 + 0.0008i


Obj =

   1.0e+11 *

   4.3402 + 2.2907i   0.0199 + 0.0103i


Obj =

   1.0e+11 *

   5.5973 + 1.6902i   0.0256 + 0.0076i


Obj =

   1.0e+11 *

   2.7293 + 3.0808i   0.0127 + 0.0139i


Obj =

   1.0e+11 *

   5.0123 + 1.5909i   0.0229 + 0.0072i


Obj =

   1.0e+11 *

   4.0736 + 1.6777i   0.0187 + 0.0076i


Obj =

   1.0e+11 *

   8.2966 + 1.5221i   0.0377 + 0.0069i


Obj =

   1.0e+11 *

   6.3667 + 1.6824i   0.0290 + 0.0076i


Obj =

   1.0e+11 *

   3.6570 + 2.2086i   0.0169 + 0.0100i


Obj =

   1.0e+12 *

   2.1831 + 0.1903i   0.0099 + 0.0009i


Obj =

   1.0e+11 *

   5.5108 + 3.2337i   0.0253 + 0.0146i


Obj =

   1.0e+12 *

   1.1843 + 0.2196i   0.0054 + 0.0010i


Obj =

   1.0e+11 *

   7.1247 + 1.5626i   0.0325 + 0.0070i


Obj =

   1.0e+11 *

   3.0163 + 3.1983i   0.0140 + 0.0144i


Obj =

   1.0e+11 *

   2.9912 + 3.3404i   0.0139 + 0.0151i


Obj =

   1.0e+12 *

   1.2569 + 0.2216i   0.0057 + 0.0010i


Obj =

   1.0e+12 *

   1.0743 + 0.1343i   0.0049 + 0.0006i


Obj =

   1.0e+11 *

   6.3668 + 1.7646i   0.0291 + 0.0080i


Obj =

   1.0e+11 *

   6.3229 + 2.1445i   0.0289 + 0.0097i


Obj =

   1.0e+11 *

   4.0615 + 2.5677i   0.0187 + 0.0116i


Obj =

   1.0e+11 *

   5.0402 + 1.7757i   0.0231 + 0.0080i


Obj =

   1.0e+11 *

   7.4331 + 2.5849i   0.0339 + 0.0116i


Obj =

   1.0e+11 *

   4.5437 + 2.8134i   0.0209 + 0.0127i


Obj =

   1.0e+12 *

   7.7264 + 0.1666i   0.0348 + 0.0008i


Obj =

   1.0e+11 *

   3.9587 + 1.3630i   0.0182 + 0.0061i


Obj =

   1.0e+12 *

   1.3352 + 0.1866i   0.0061 + 0.0008i


Obj =

   1.0e+12 *

   1.3655 + 0.1923i   0.0062 + 0.0009i


Obj =

   1.0e+11 *

   6.8164 + 2.1813i   0.0311 + 0.0098i


Obj =

   1.0e+11 *

   6.9803 + 2.3751i   0.0318 + 0.0107i


Obj =

   1.0e+11 *

   8.7241 + 1.2626i   0.0397 + 0.0057i


Obj =

   1.0e+11 *

   9.4473 + 1.7764i   0.0429 + 0.0080i


Obj =

   1.0e+11 *

   2.9838 + 2.9953i   0.0139 + 0.0135i


Obj =

   1.0e+11 *

   4.7738 + 1.0734i   0.0218 + 0.0048i


Obj =

   1.0e+11 *

   9.5048 + 3.2442i   0.0433 + 0.0146i


Obj =

   1.0e+11 *

   3.4871 + 2.8526i   0.0161 + 0.0129i


Obj =

   1.0e+11 *

   7.8562 + 2.3313i   0.0358 + 0.0105i


Obj =

   1.0e+11 *

   4.8092 + 2.5321i   0.0221 + 0.0114i


Obj =

   1.0e+11 *

   5.7507 + 1.7087i   0.0263 + 0.0077i


Obj =

   1.0e+11 *

   3.3361 + 2.2627i   0.0154 + 0.0102i


Obj =

   1.0e+11 *

   4.4049 + 3.3091i   0.0203 + 0.0149i


Obj =

   1.0e+11 *

   3.0744 + 2.4623i   0.0142 + 0.0111i

Warning: One or more feasible individuals has a complex fitness function value. gamultiobj is using the real part of the
fitness function values 
> In rankAndDistance (line 21)
In gamultiobjMakeState (line 199)
In gamultiobjsolve (line 20)
In gamultiobj (line 304)
In Main_Genetic_Algorithm (line 41) 
Warning: Imaginary parts of complex X and/or Y arguments ignored. 
> In gaplotpareto>plotFirstFront (line 72)
In gaplotpareto (line 28)
In gadsplot>callOnePlotFcn (line 194)
In gadsplot (line 141)
In gamultiobjsolve (line 24)
In gamultiobj (line 304)
In Main_Genetic_Algorithm (line 41) 
Warning: Using only the real component of complex data. 
> In matlab.graphics.chart.internal.getRealData (line 52)
In bar (line 139)
In gaplotscores (line 41)
In gadsplot>callOnePlotFcn (line 194)
In gadsplot (line 141)
In gamultiobjsolve (line 24)
In gamultiobj (line 304)
In Main_Genetic_Algorithm (line 41) 

Obj =

   1.0e+12 *

   1.1881 + 0.2512i   0.0054 + 0.0011i


Obj =

   1.0e+11 *

   3.1144 + 3.0470i   0.0145 + 0.0137i


Obj =

   1.0e+11 *

   3.7438 + 2.8514i   0.0173 + 0.0128i


Obj =

   1.0e+11 *

   3.0983 + 3.0006i   0.0144 + 0.0135i


Obj =

   1.0e+11 *

   4.5208 + 1.6571i   0.0207 + 0.0075i


Obj =

   1.0e+11 *

   3.0157 + 2.9600i   0.0140 + 0.0133i


Obj =

   1.0e+11 *

   4.2461 + 1.4659i   0.0195 + 0.0066i


Obj =

   1.0e+11 *

   7.9336 + 2.3053i   0.0361 + 0.0104i


Obj =

   1.0e+11 *

   6.1800 + 2.1070i   0.0282 + 0.0095i


Obj =

   1.0e+11 *

   2.9913 + 3.3399i   0.0139 + 0.0150i


Obj =

   1.0e+11 *

   4.2126 + 1.9457i   0.0194 + 0.0088i


Obj =

   1.0e+11 *

   3.7206 + 2.2488i   0.0171 + 0.0101i


Obj =

   1.0e+11 *

   3.9526 + 1.8088i   0.0182 + 0.0081i


Obj =

   1.0e+11 *

   6.5851 + 1.6861i   0.0300 + 0.0076i


Obj =

   1.0e+11 *

   5.0697 + 1.6949i   0.0232 + 0.0076i


Obj =

   1.0e+11 *

   9.2479 + 1.5526i   0.0420 + 0.0070i


Obj =

   1.0e+11 *

   3.1204 + 3.0248i   0.0145 + 0.0136i


Obj =

   1.0e+11 *

   3.6741 + 2.1323i   0.0169 + 0.0096i


Obj =

   1.0e+11 *

   3.9378 + 2.4991i   0.0181 + 0.0113i


Obj =

   1.0e+11 *

   4.5609 + 1.5928i   0.0209 + 0.0072i


Obj =

   1.0e+11 *

   3.8672 + 1.5589i   0.0178 + 0.0070i


Obj =

   1.0e+11 *

   3.2057 + 2.6197i   0.0149 + 0.0118i


Obj =

   1.0e+11 *

   3.8968 + 1.9892i   0.0179 + 0.0090i


Obj =

   1.0e+11 *

   5.5084 + 2.1275i   0.0252 + 0.0096i


Obj =

   1.0e+11 *

   3.2324 + 2.5177i   0.0150 + 0.0113i


Obj =

   1.0e+11 *

   4.1460 + 2.3829i   0.0191 + 0.0107i


Obj =

   1.0e+11 *

   3.1253 + 2.9068i   0.0145 + 0.0131i


Obj =

   1.0e+11 *

   3.0991 + 3.2528i   0.0144 + 0.0147i


Obj =

   1.0e+11 *

   6.4726 + 2.5683i   0.0296 + 0.0116i


Obj =

   1.0e+11 *

   4.1689 + 2.1560i   0.0192 + 0.0097i


Obj =

   1.0e+11 *

   8.2382 + 1.2482i   0.0375 + 0.0056i


Obj =

   1.0e+11 *

   3.2638 + 2.9144i   0.0151 + 0.0131i


Obj =

   1.0e+11 *

   3.2686 + 3.0957i   0.0152 + 0.0139i


Obj =

   1.0e+11 *

   3.7187 + 2.2086i   0.0171 + 0.0100i


Obj =

   1.0e+11 *

   3.9584 + 1.3633i   0.0182 + 0.0061i


Obj =

   1.0e+11 *

   3.9483 + 1.3630i   0.0181 + 0.0061i


Obj =

   1.0e+11 *

   3.5312 + 3.1425i   0.0163 + 0.0142i


Obj =

   1.0e+12 *

   1.2013 + 0.2470i   0.0055 + 0.0011i


Obj =

   1.0e+11 *

   3.3136 + 2.2630i   0.0153 + 0.0102i


Obj =

   1.0e+11 *

   5.0223 + 1.7757i   0.0230 + 0.0080i


Obj =

   1.0e+11 *

   8.4716 + 1.5626i   0.0385 + 0.0070i


Obj =

   1.0e+11 *

   8.2965 + 1.5221i   0.0377 + 0.0069i


Obj =

   1.0e+11 *

   3.3401 + 2.5507i   0.0155 + 0.0115i


Obj =

   1.0e+12 *

   1.3632 + 0.2196i   0.0062 + 0.0010i


Obj =

   1.0e+11 *

   2.7293 + 3.0808i   0.0127 + 0.0139i


Obj =

   1.0e+11 *

   7.1247 + 1.5626i   0.0325 + 0.0070i


Obj =

   1.0e+11 *

   4.0413 + 2.4315i   0.0186 + 0.0110i


Obj =

   1.0e+11 *

   7.3096 + 2.5849i   0.0333 + 0.0116i


Obj =

   1.0e+11 *

   3.7187 + 2.2086i   0.0171 + 0.0100i


Obj =

   1.0e+11 *

   2.9798 + 2.9953i   0.0139 + 0.0135i

Error using matlab.graphics.chart.primitive.Line/set
Complex values are not supported.

Error in gaplotpareto>plotFirstFront (line 84)
            set(plotHandle,'Xdata',xy(:,1), 'Ydata',xy(:,2));

Error in gaplotpareto (line 28)
    plotFirstFront(state.Score(range,:),state.Rank(range),markers{1 + mod(i,5)},objectivesToPlot(:)',flag,tag);

Error in gadsplot>callOnePlotFcn (line 194)
        optimvalues = plotfcn(varargin{1:end});

Error in gadsplot (line 148)
        [state,optimvalues] =
        callOnePlotFcn(fname,plotNames{i},state,options.OutputPlotFcnOptions,optimvalues,flag,args{i}{:});

Error in gamultiobjsolve (line 64)
        state = gadsplot(options,state,currentState,'Genetic Algorithm');

Error in gamultiobj (line 304)
[x,fval,exitFlag,output,population,scores] = gamultiobjsolve(FitnessFcn,nvars, ...

Error in Main_Genetic_Algorithm (line 41)
[X, fval, exitflag, output, population, scores] = gamultiobj(@Fitness_Function, ...

[Walter Roberson]

You fix your objective function so that it never returns complex values, or you set up constraints so that you avoid the area in which it would return complex values. or you use some other kind of multi-objective minimizer that supports complex values.