This example shows how to minimize an objective function subject to nonlinear inequality constraints and bounds using pattern search.

We want to minimize a simple objective function of two variables `x1`

and `x2`

min f(x) = (4 - 2.1*x1^2 + x1^4/3)*x1^2 + x1*x2 + (-4 + 4*x2^2)*x2^2; x

such that the following nonlinear constraints and bounds are satisfied

x1*x2 + x1 - x2 + 1.5 <=0, (nonlinear constraint) 10 - x1*x2 <=0, (nonlinear constraint) 0 <= x1 <= 1, and (bound) 0 <= x2 <= 13 (bound)

The above objective function is known as 'cam' as described in L.C.W. Dixon and G.P. Szego (eds.), Towards Global Optimisation 2, North-Holland, Amsterdam, 1978.

We create a MATLAB file named simple_objective.m with the following code in it:

function y = simple_objective(x) y = (4 - 2.1*x(1)^2 + x(1)^4/3)*x(1)^2 + x(1)*x(2) + ... (-4 + 4*x(2)^2)*x(2)^2;

The Pattern Search solver assumes the objective function will take one input `x`

where `x`

has as many elements as number of variables in the problem. The objective function computes the value of the function and returns that scalar value in its one return argument `y`

.

We create a MATLAB file named `simple_constraint.m`

with the following code in it:

function [c, ceq] = simple_constraint(x) c = [1.5 + x(1)*x(2) + x(1) - x(2); -x(1)*x(2) + 10]; ceq = [];

The Pattern Search solver assumes the constraint function will take one input `x`

where `x`

has as many elements as number of variables in the problem. The constraint function computes the values of all the inequality and equality constraints and returns two vectors `c`

and `ceq`

respectively.

To minimize our objective function using the `patternsearch`

function, we need to pass in a function handle to the objective function as well as specifying a start point as the second argument. Lower and upper bounds are provided as `LB`

and `UB`

respectively. In addition, we also need to pass in a function handle to the nonlinear constraint function.

ObjectiveFunction = @simple_objective; X0 = [0 0]; % Starting point LB = [0 0]; % Lower bound UB = [1 13]; % Upper bound ConstraintFunction = @simple_constraint; [x,fval] = patternsearch(ObjectiveFunction,X0,[],[],[],[],LB,UB, ... ConstraintFunction)

Optimization terminated: mesh size less than options.MeshTolerance and constraint violation is less than options.ConstraintTolerance. x = 0.8122 12.3122 fval = 9.1324e+04

Next we create options using `optimoptions`

that select two plot functions. The first plot function `psplotbestf`

plots the best objective function value at every iterations, and the second plot function `psplotmaxconstr`

plots maximum constraint violation at every iterations. We can also visualize the progress of the algorithm by displaying information to the command window using the `Display`

option.

options = optimoptions(@patternsearch,'PlotFcn',{@psplotbestf,@psplotmaxconstr}, ... 'Display','iter'); % Next we run the PATTERNSEARCH solver. [x,fval] = patternsearch(ObjectiveFunction,X0,[],[],[],[],LB,UB, ... ConstraintFunction,options)

max Iter f-count f(x) constraint MeshSize Method 0 1 0 10 0.8919 1 28 113580 0 0.001 Increase penalty 2 105 91324 1.788e-07 1e-05 Increase penalty 3 192 91324 1.187e-11 1e-07 Increase penalty Optimization terminated: mesh size less than options.MeshTolerance and constraint violation is less than options.ConstraintTolerance. x = 0.8122 12.3122 fval = 9.1324e+04

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