This example shows how to use the Optimization app with the
to minimize a quadratic subject to linear and nonlinear constraints
The Optimization app warns that it will be removed in a future release.
Consider the problem of finding [x1, x2] that solves
subject to the constraints
The starting guess for this problem is x1 = 3 and x2 = 1.
function f = objecfun(x) f = x(1)^2 + x(2)^2;
function [c,ceq] = nonlconstr(x) c = [-x(1)^2 - x(2)^2 + 1; -9*x(1)^2 - x(2)^2 + 9; -x(1)^2 + x(2); -x(2)^2 + x(1)]; ceq = ;
optimtool in the Command
Window to open the Optimization app.
fmincon from the
selection of solvers and change the Algorithm field
@objecfun in the Objective
function field to call the
[3;1] in the Start
Define the constraints.
Set the bound
0.5 ≤ x1 by
[0.5,-Inf] in the Lower field.
-Inf entry means there is no lower bound on x2.
Set the linear inequality constraint by entering
-1] in the A field and enter
the b field.
Set the nonlinear constraints by entering
the Nonlinear constraint function field.
In the Options pane, expand the Display
to command window option if necessary, and select
show algorithm information at the Command Window for each iteration.
Click the Start button as shown in the following figure.
When the algorithm terminates, under Run solver and view results the following information is displayed:
The Current iteration value when
the algorithm terminated, which for this example is
The final value of the objective function when the algorithm terminated:
Objective function value: 2.0000000268595803
The algorithm termination message:
Local minimum found that satisfies the constraints. Optimization completed because the objective function is non-decreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance.
The final point, which for this example is
In the Command Window, the algorithm information is displayed for each iteration:
Max Line search Directional First-order Iter F-count f(x) constraint steplength derivative optimality Procedure 0 3 10 2 Infeasible start point 1 6 4.84298 -0.1322 1 -5.22 1.74 2 9 4.0251 -0.01168 1 -4.39 4.08 Hessian modified twice 3 12 2.42704 -0.03214 1 -3.85 1.09 4 15 2.03615 -0.004728 1 -3.04 0.995 Hessian modified twice 5 18 2.00033 -5.596e-05 1 -2.82 0.0664 Hessian modified twice 6 21 2 -5.326e-09 1 -2.81 0.000522 Hessian modified twice Active inequalities (to within options.ConstraintTolerance = 1e-06): lower upper ineqlin ineqnonlin 3 4 Local minimum found that satisfies the constraints. Optimization completed because the objective function is non-decreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance.