For some problems, you might want output from an optimization algorithm at each iteration. For example, you might want to find the sequence of points that the algorithm computes and plot those points. To do this, create an output function that the optimization function calls at each iteration. See Output Function for details and syntax.
Generally, the solvers that can employ an output function are the ones that can take nonlinear functions as inputs. You can determine which solvers can have an output function by looking in the Options section of function reference pages, or by checking whether the Output function option is available in the Optimization app for a solver.
The following example continues the one in Nonlinear Inequality Constraints, which calls the
fmincon at the command line to solve a
nonlinear, constrained optimization problem. The example in this section
uses a function file to call
fmincon. The file
also contains all the functions needed for the example, including:
The objective function
The constraint function
An output function that records the history of points
computed by the algorithm for
fmincon. At each
iteration of the algorithm for
fmincon, the output
Plots the current point computed by the algorithm.
Stores the point and its corresponding objective function
value in a variable called
history, and stores
the current search direction in a variable called
The search direction is a vector that points in the direction from
the current point to the next one.
The code for the file is here: Writing the Example Function File.
You specify the output function in
options = optimoptions(@fmincon,'OutputFcn',@outfun)
outfun is the name of the output function.
When you call an optimization function with
an input, the optimization function calls
each iteration of its algorithm.
outfun can be any MATLAB® function,
but in this example, it is a nested function of the function file
described in Writing the Example Function File.
The following code defines the output function:
function stop = outfun(x,optimValues,state) stop = false; switch state case 'init' hold on case 'iter' % Concatenate current point and objective function % value with history. x must be a row vector. history.fval = [history.fval; optimValues.fval]; history.x = [history.x; x]; % Concatenate current search direction with % searchdir. searchdir = [searchdir;... optimValues.searchdirection']; plot(x(1),x(2),'o'); % Label points with iteration number. % Add .15 to x(1) to separate label from plotted 'o' text(x(1)+.15,x(2),num2str(optimValues.iteration)); case 'done' hold off otherwise end end
See Using Handles to Store Function Parameters in the MATLAB Programming Fundamentals documentation for more information about nested functions.
The arguments that the optimization function passes to
x — The point computed by
the algorithm at the current iteration
optimValues — Structure
containing data from the current iteration
The example uses the following fields of
Number of the current iteration
optimValues.fval — Current
objective function value
Current search direction
state — The current state
of the algorithm (
For more information about these arguments, see Output Function.
To create the function file for this example:
Open a new file in the MATLAB Editor.
Copy and paste the following code into the file:
function [history,searchdir] = runfmincon % Set up shared variables with OUTFUN history.x = ; history.fval = ; searchdir = ; % call optimization x0 = [-1 1]; options = optimoptions(@fmincon,'OutputFcn',@outfun,... 'Display','iter','Algorithm','active-set'); xsol = fmincon(@objfun,x0,,,,,,,@confun,options); function stop = outfun(x,optimValues,state) stop = false; switch state case 'init' hold on case 'iter' % Concatenate current point and objective function % value with history. x must be a row vector. history.fval = [history.fval; optimValues.fval]; history.x = [history.x; x]; % Concatenate current search direction with % searchdir. searchdir = [searchdir;... optimValues.searchdirection']; plot(x(1),x(2),'o'); % Label points with iteration number and add title. % Add .15 to x(1) to separate label from plotted 'o' text(x(1)+.15,x(2),... num2str(optimValues.iteration)); title('Sequence of Points Computed by fmincon'); case 'done' hold off otherwise end end function f = objfun(x) f = exp(x(1))*(4*x(1)^2 + 2*x(2)^2 + 4*x(1)*x(2) +... 2*x(2) + 1); end function [c, ceq] = confun(x) % Nonlinear inequality constraints c = [1.5 + x(1)*x(2) - x(1) - x(2); -x(1)*x(2) - 10]; % Nonlinear equality constraints ceq = ; end end
Save the file as
a folder on the MATLAB path.
To run the example, enter:
[history searchdir] = runfmincon;
This displays the following iterative output in the Command Window.
Max Line search Directional First-order Iter F-count f(x) constraint steplength derivative optimality Procedure 0 3 1.8394 0.5 Infeasible 1 6 1.85127 -0.09197 1 0.109 0.778 start point 2 9 0.300167 9.33 1 -0.117 0.313 Hessian modified 3 12 0.529835 0.9209 1 0.12 0.232 twice 4 16 0.186965 -1.517 0.5 -0.224 0.13 5 19 0.0729085 0.3313 1 -0.121 0.054 6 22 0.0353323 -0.03303 1 -0.0542 0.0271 7 25 0.0235566 0.003184 1 -0.0271 0.00587 8 28 0.0235504 9.032e-008 1 -0.0146 8.51e-007 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. Active inequalities (to within options.ConstraintTolerance = 1e-006): lower upper ineqlin ineqnonlin 1 2
history is a structure that contains
history = struct with fields: x: [9×2 double] fval: [9×1 double]
fval field contains the objective function
values corresponding to the sequence of points computed by
history.fval ans = 1.8394 1.8513 0.3002 0.5298 0.1870 0.0729 0.0353 0.0236 0.0236
These are the same values displayed in the iterative output
in the column with header
x field of
the sequence of points computed by the algorithm:
history.x ans = -1.0000 1.0000 -1.3679 1.2500 -5.5708 3.4699 -4.8000 2.2752 -6.7054 1.2618 -8.0679 1.0186 -9.0230 1.0532 -9.5471 1.0471 -9.5474 1.0474
This example displays a plot of this sequence of points, in which each point is labeled by its iteration number.
The optimal point occurs at the eighth iteration. Note that the last two points in the sequence are so close that they overlap.
The second output argument,
the search directions for
fmincon at each iteration.
The search direction is a vector pointing from the point computed
at the current iteration to the point computed at the next iteration:
searchdir = -0.3679 0.2500 -4.2029 2.2199 0.7708 -1.1947 -3.8108 -2.0268 -1.3625 -0.2432 -0.9552 0.0346 -0.5241 -0.0061 -0.0003 0.0003