Consider the problem of finding a set of values [x1, x2] that solves
To solve this two-dimensional problem, write a file that returns the function value. Then, invoke the unconstrained minimization routine fminunc.
This code ships with the toolbox. To view, enter type objfun:
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);
x0 = [-1,1]; % Starting guess options = optimoptions(@fminunc,'Algorithm','quasi-newton'); [x,fval,exitflag,output] = fminunc(@objfun,x0,options);
This produces the following output:
Local minimum found. Optimization completed because the size of the gradient is less than the default value of the function tolerance.
View the results:
x,fval,exitflag,output x = 0.5000 -1.0000 fval = 3.6609e-15 exitflag = 1 output = iterations: 8 funcCount: 66 stepsize: 1 firstorderopt: 1.2284e-007 algorithm: 'medium-scale: Quasi-Newton line search' message: [1x436 char]
The exitflag tells whether the algorithm converged. exitflag = 1 means a local minimum was found. The meanings of exitflags are given in function reference pages.
The output structure gives more details about the optimization. For fminunc, it includes the number of iterations in iterations, the number of function evaluations in funcCount, the final step-size in stepsize, a measure of first-order optimality (which in this unconstrained case is the infinity norm of the gradient at the solution) in firstorderopt, the type of algorithm used in algorithm, and the exit message (the reason the algorithm stopped).
Pass the variable options to fminunc to change characteristics of the optimization algorithm, as in
x = fminunc(@objfun,x0,options);
options contains values for termination tolerances and algorithm choices. Create options using the optimoptions function:
options = optimoptions(@fminunc,'Algorithm','quasi-newton');
You can also create options by exporting from the Optimization app.
In this example, we have used the quasi-newton algorithm. Other options include controlling the amount of command line display during the optimization iteration, the tolerances for the termination criteria, whether a user-supplied gradient or Jacobian is to be used, and the maximum number of iterations or function evaluations. See optimoptions, the individual optimization functions, and Optimization Options Reference for more options and information.