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You can plot various measures of progress during the execution
of a solver. Set the `PlotFcns` name-value pair in `optimoptions`, and specify one or more plotting
functions for the solver to call at each iteration. Pass a function
handle or cell array of function handles.

There are a variety of predefined plot functions available. See:

The

`PlotFcns`option description in the solver function reference page**Optimization app > Options > Plot functions**

You can also use a custom-written plot function. Write a function file using the same structure as an output function. For more information on this structure, see Output Function.

This example shows how to use a plot function to view the progress
of the `fmincon` interior-point algorithm. The
problem is taken from the Getting Started Solve a Constrained Nonlinear Problem. The first part
of the example shows how to run the optimization using the Optimization
app. The second part shows how to run the optimization from the command
line.

Write the nonlinear objective and constraint functions, including the derivatives:

function [f g H] = rosenboth(x) % ROSENBOTH returns both the value y of Rosenbrock's function % and also the value g of its gradient and H the Hessian. f = 100*(x(2) - x(1)^2)^2 + (1-x(1))^2; if nargout > 1 g = [-400*(x(2)-x(1)^2)*x(1)-2*(1-x(1)); 200*(x(2)-x(1)^2)]; if nargout > 2 H = [1200*x(1)^2-400*x(2)+2, -400*x(1); -400*x(1), 200]; end end

Save this file as

`rosenboth.m`.function [c,ceq,gc,gceq] = unitdisk2(x) % UNITDISK2 returns the value of the constraint % function for the disk of radius 1 centered at % [0 0]. It also returns the gradient. c = x(1)^2 + x(2)^2 - 1; ceq = [ ]; if nargout > 2 gc = [2*x(1);2*x(2)]; gceq = []; end

Save this file as

`unitdisk2.m`.Start the Optimization app by entering

`optimtool`at the command line.Set up the optimization:

Choose the

`fmincon`solver.Choose the

`Interior point`algorithm.Set the objective function to

`@rosenboth`.Choose

`Gradient supplied`for the objective function derivative.Set the start point to

`[0 0]`.Set the nonlinear constraint function to

`@unitdisk2`.Choose

`Gradient supplied`for the nonlinear constraint derivatives.

Your

**Problem Setup and Results**panel should match the following figure.Choose three plot functions in the

**Options**pane:**Current point**,**Function value**, and**First order optimality**.Click the

**Start**button under**Run solver and view results**.The output appears as follows in the Optimization app.

In addition, the following three plots appear in a separate window.

The "Current Point" plot graphically shows the minimizer

`[0.786,0.618]`, which is reported as the**Final point**in the**Run solver and view results**pane. This plot updates at each iteration, showing the intermediate iterates.The "Current Function Value" plot shows the objective function value at all iterations. This graph is nearly monotone, showing

`fmincon`reduces the objective function at almost every iteration.The "First-order Optimality" plot shows the first-order optimality measure at all iterations.

Write the nonlinear objective and constraint functions, including the derivatives, as shown in Running the Optimization Using the Optimization App.

Create an options structure that includes calling the three plot functions:

options = optimoptions(@fmincon,'Algorithm','interior-point',... 'GradObj','on','GradConstr','on','PlotFcns',{@optimplotx,... @optimplotfval,@optimplotfirstorderopt});

Call

`fmincon`:x = fmincon(@rosenboth,[0 0],[],[],[],[],[],[],... @unitdisk2,options)

`fmincon`gives the following output:Local minimum found that satisfies the constraints. Optimization completed because the objective function is non-decreasing in feasible directions, to within the default value of the function tolerance, and constraints are satisfied to within the default value of the constraint tolerance. x = 0.7864 0.6177

`fmincon`also displays the three plot functions, shown at the end of Running the Optimization Using the Optimization App.

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