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Some problems require you to find a point that satisfies all constraints, with no objective function to minimize. For example, suppose that you have the following constraints:

$$\begin{array}{l}(y+{x}^{2}{)}^{2}+0.1{y}^{2}\le 1\\ y\le \mathrm{exp}(-x)-3\\ y\le x-4.\end{array}$$

Do any points $$(x,y)$$ satisfy the constraints? To find out, write a function that returns the constraints in a structure field `Ineq`

. Write the constraints in terms of a two-element vector $$x=({x}_{1},{x}_{2})$$ instead of $$(x,y)$$. Write each inequality as a function $$c(x)$$, meaning the inequalities $$c(x)\le 0$$, by subtracting the right side of each inequality from both sides. To enable plotting, write the function in a vectorized manner, where each row represents one point. The code for this helper function, named `objconstr`

, appears at the end of this example.

Plot the points where the three functions satisfy equalities for $$-2\le x\le 2$$ and $$-4\le y\le 2$$, and indicate the inequalities by plotting level lines for function values equal to –1/2.

[XX,YY] = meshgrid(-2:0.1:2,-4:0.1:2); ZZ = objconstr([XX(:),YY(:)]).Ineq; ZZ = reshape(ZZ,[size(XX),3]); h = figure; ax = gca; contour(ax,XX,YY,ZZ(:,:,1),[-1/2 0],'r','ShowText','on'); hold on contour(ax,XX,YY,ZZ(:,:,2),[-1/2 0],'k','ShowText','on'); contour(ax,XX,YY,ZZ(:,:,3),[-1/2 0],'b','ShowText','on'); hold off

The plot shows that feasible points exist near [1.75,–3].

Set lower bounds of –5 and upper bounds of 3, and solve the problem using `surrogateopt`

.

```
rng(1) % For reproducibility
lb = [-5,-5];
ub = [3,3];
[x,fval,exitflag,output,trials] = surrogateopt(@objconstr,lb,ub)
```

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

`x = `*1×2*
1.7689 -3.0340

fval = 1x0 empty double row vector

exitflag = 0

`output = `*struct with fields:*
elapsedtime: 32.8595
funccount: 200
constrviolation: -0.0705
ineq: [-0.0705 -0.2046 -0.8029]
rngstate: [1x1 struct]
message: 'Surrogateopt stopped because it exceeded the function evaluation limit set by ...'

`trials = `*struct with fields:*
X: [200x2 double]
Ineq: [200x3 double]

Check the feasibility at the returned solution `x`

.

disp(output.ineq)

-0.0705 -0.2046 -0.8029

Equivalently, evaluate the function `objconstr`

at the returned solution `x`

.

disp(objconstr(x).Ineq)

-0.0705 -0.2046 -0.8029

Equivalently, examine the `Ineq`

field in the `trials`

structure for the solution `x`

. First, find the index of `x`

in the `trials.X`

field.

```
indx = ismember(trials.X,x,'rows');
disp(trials.Ineq(indx,:))
```

-0.0705 -0.2046 -0.8029

All constraint function values are negative, indicating that the point `x`

is feasible.

View the feasible points evaluated by `surrogateopt`

.

opts = optimoptions("surrogateopt"); indx = max(trials.Ineq,[],2) <= opts.ConstraintTolerance; % Indices of feasible points figure(h); hold on plot(trials.X(indx,1),trials.X(indx,2),'*') xlim([1 2]) ylim([-3.5 -2.5]) hold off

This code creates the `objconstr`

helper function.

function f = objconstr(x) c(:,1) = (x(:,2) + x(:,1).^2).^2 + 0.1*x(:,2).^2 - 1; c(:,2) = x(:,2) - exp(-x(:,1)) + 3; c(:,3) = x(:,2) - x(:,1) + 4; f.Ineq = c; end