# Mixed-Integer Surrogate Optimization, Problem-Based

This example shows how to solve an optimization problem that involves integer variables. In this example, find the point `x`

that minimizes the `multirosenbrock`

function over integer-valued arguments ranging from –3 to 6 in 10 dimensions. The `multirosenbrock`

function is a poorly scaled function that is difficult to optimize. Its minimum value is 0, which is attained at the point `[1,1,...,1]`

. The code for the `multirosenbrock`

function appears at the end of this example.

Create a 10-D row vector variable `x`

of type integer with bounds –3 to 6. When you specify scalar bounds, the bounds apply to all variable components.

x = optimvar("x",1,10,"LowerBound",-3,"UpperBound",6,"Type","integer");

To use `multirosenbrock`

as the objective function, convert the function to an optimization expression using `fcn2optimexpr`

.

fun = fcn2optimexpr(@multirosenbrock,x);

Create an optimization problem with the objective function `multirosenbrock`

.

`prob = optimproblem("Objective",fun);`

Set the maximum number of function evaluations to 200.

opts = optimoptions("surrogateopt","MaxFunctionEvaluations",200);

Solve the problem.

rng(1,'twister') % For reproducibility [sol,fval] = solve(prob,"Solver","surrogateopt","Options",opts)

Solving problem using surrogateopt.

surrogateopt stopped because it exceeded the function evaluation limit set by 'options.MaxFunctionEvaluations'.

`sol = `*struct with fields:*
x: [1 1 1 1 1 1 1 1 1 1]

fval = 0

In this case, `surrogateopt`

reaches the correct solution.

### Mixed-Integer Problem

Suppose that only the first six variables are integer-valued. To reformulate the problem, create a 6-D integer variable `xint`

and a 4-D continuous variable `xcont`

.

xint = optimvar("xint",1,6,"LowerBound",-3,"UpperBound",6,"Type","integer"); xcont = optimvar("xcont",1,4,"LowerBound",-3,"UpperBound",6);

Convert `multirosenbrock`

to an optimization expression using the input `[xint xcont]`

.

fun2 = fcn2optimexpr(@multirosenbrock,[xint xcont]);

Create and solve the problem.

prob2 = optimproblem("Objective",fun2); rng(1,'twister') % For reproducibility [sol2,fval2] = solve(prob2,"Solver","surrogateopt","Options",opts)

Solving problem using surrogateopt.

surrogateopt stopped because it exceeded the function evaluation limit set by 'options.MaxFunctionEvaluations'.

`sol2 = `*struct with fields:*
xcont: [1.0496 1.1061 1.0507 1.1050]
xint: [1 1 1 1 1 1]

fval2 = 0.0071

This time the integer variables reach the correct solution, and the continuous variables are near the solution, but are not completely accurate.

### Helper Function

This code creates the `multirosenbrock`

helper function.

function F = multirosenbrock(x) % This function is a multidimensional generalization of Rosenbrock's % function. It operates in a vectorized manner, assuming that x is a matrix % whose rows are the individuals. % Copyright 2014 by The MathWorks, Inc. N = size(x,2); % assumes x is a row vector or 2-D matrix if mod(N,2) % if N is odd error('Input rows must have an even number of elements') end odds = 1:2:N-1; evens = 2:2:N; F = zeros(size(x)); F(:,odds) = 1-x(:,odds); F(:,evens) = 10*(x(:,evens)-x(:,odds).^2); F = sum(F.^2,2); end