This example shows how to solve a constrained nonlinear problem using an Optimization Toolbox™ solver. The example demonstrates the typical work flow: create an objective function, create constraints, solve the problem, and examine the results.
over the unit disk, that is, the disk of radius 1 centered at the origin. In other words, find x that minimizes the function f(x) over the set . This problem is a minimization of a nonlinear function with a nonlinear constraint.
Rosenbrock's function is a standard test function in optimization. It has a
unique minimum value of 0 attained at the point
Finding the minimum is a challenge for some algorithms because the function has
a shallow minimum inside a deeply curved valley. The solution for this problem
is not at the point
[1,1] because that point does not satisfy
This figure shows two views of Rosenbrock's function in the unit disk. The vertical axis is log-scaled; in other words, the plot shows log(1+f(x)). Contour lines lie beneath the surface plot.
Rosenbrock's Function, Log-Scaled: Two Views.
The function f(x) is called the objective function. The objective function is the function you want to minimize. The inequality is called a constraint. Constraints limit the set of x over which a solver searches for a minimum. You can have any number of constraints, which are inequalities or equations.
All Optimization Toolbox optimization functions minimize an objective function. To maximize a function f, apply an optimization routine to minimize –f. For more details about maximizing, see Maximizing an Objective.
To use Optimization Toolbox software, express your problem as follows:
Define the objective function in the MATLAB® language, as a function file or anonymous function. This example uses a function file.
Define the constraints as a separate file or anonymous function.
A function file is a text file that contains MATLAB commands and has the extension
.m. Create a
function file in any text editor, or use the built-in MATLAB Editor as in this example.
At the command line, enter:
In the MATLAB Editor, enter:
%% ROSENBROCK(x) expects a two-column matrix and returns a column vector % The output is the Rosenbrock function, which has a minimum at % (1,1) of value 0, and is strictly positive everywhere else. function f = rosenbrock(x) f = 100*(x(:,2) - x(:,1).^2).^2 + (1 - x(:,1)).^2;
rosenbrock is a vectorized function that can
compute values for several points at once. See Vectorization (MATLAB). A vectorized function is
best for plotting. For a nonvectorized version, enter:
%% ROSENBROCK1(x) expects a two-element vector and returns a scalar % The output is the Rosenbrock function, which has a minimum at % (1,1) of value 0, and is strictly positive everywhere else. function f = rosenbrock1(x) f = 100*(x(2) - x(1)^2)^2 + (1 - x(1))^2;
Save the file with name
Constraint functions have the form c(x) ≤ 0 or ceq(x) = 0. The constraint is not in the form that the solver handles. To have the correct syntax, reformulate the constraint as .
Furthermore, the syntax for nonlinear constraints returns both equality and
inequality constraints. This example includes only an inequality constraint, so
you must pass an empty array
 as the equality constraint
With these considerations in mind, write a function file for the nonlinear constraint.
Create a file named
unitdisk.m containing the
function [c,ceq] = unitdisk(x) c = x(1)^2 + x(2)^2 - 1; ceq = [ ];
Save the file
There are two ways to run the optimization:
Use the Optimization app; see Minimize Rosenbrock's Function Using the Optimization App.
Use command-line functions; see Minimize Rosenbrock's Function at the Command Line.
The Optimization app warns that it will be removed in a future release. For alternatives, see Optimization App Alternatives.
Start the Optimization app by entering
the command line. For more information about this tool, see Optimization App.
The default Solver,
Constrained nonlinear minimization, is selected. This
solver is appropriate for this problem because Rosenbrock's function is
nonlinear, and the problem has a constraint. For more information about
choosing a solver, see Optimization Decision Table.
The default Algorithm,
point, is also selected.
In the Objective function box, enter
@rosenbrock. The @ character indicates the function handle (MATLAB) of the file
In the Start point box, enter
0] to specify the initial point where
fmincon begins its search for a minimum.
In the Nonlinear constraint functionbox, enter
@unitdisk, the function handle of
Ensure that your Problem Setup and Results pane matches this figure.
In the Options pane, under Display to
command window (at the bottom of the pane), select
iterative from the Level of
display list. (If you do not see the option, click Display to command
window.) This setting shows the progress of
fmincon in the command window.
In the Problem Setup and Results pane, under Run solver and view results, click Start.
The following message appears in the Run solver and view results box:
Optimization running. Objective function value: 0.04567482475812774 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.
The message tells you that:
The search for a constrained optimum ended because the derivative of the objective function is nearly 0 in directions allowed by the constraint.
The constraint is satisfied to the requisite accuracy.
At the bottom of the Problem Setup and Results pane, the
x appears under Final point.
For more information about exit messages, see Exit Flags and Exit Messages.
You can run the same optimization from the command line.
Create options that choose iterative display and the
options = optimoptions(@fmincon,... 'Display','iter','Algorithm','interior-point');
fmincon solver with the
options structure, reporting both the
x of the minimizer and the value
fval attained by the objective
[x,fval] = fmincon(@rosenbrock,[0 0],... ,,,,,,@unitdisk,options)
The six sets of empty brackets represent optional constraints that
are not being used in this example. See the
reference pages for the syntax.
MATLAB outputs a table of iterations and the results of the optimization.
Local minimum found that satisfies the constraints. Optimization completed because the objective function is non-decreasing in feasible directions, to within the selected value of the function tolerance, and constraints are satisfied to within the selected value of the constraint tolerance. x = 0.7864 0.6177 fval = 0.0457
The message tells you that the search for a constrained optimum ended because the derivative of the objective function is nearly 0 in directions allowed by the constraint, and that the constraint is satisfied to the requisite accuracy. Several phrases in the message contain links to more information about the terms used in the message. For more details about these links, see Enhanced Exit Messages.
The iteration table in the command window shows how MATLAB searched for the minimum value of Rosenbrock's function in the unit disk. This table is the same whether you use the Optimization app or the command line. MATLAB reports the minimization as follows:
First-order Norm of Iter F-count f(x) Feasibility optimality step 0 3 1.000000e+00 0.000e+00 2.000e+00 1 13 7.753537e-01 0.000e+00 6.250e+00 1.768e-01 2 18 6.519648e-01 0.000e+00 9.048e+00 1.679e-01 3 21 5.543209e-01 0.000e+00 8.033e+00 1.203e-01 4 24 2.985207e-01 0.000e+00 1.790e+00 9.328e-02 5 27 2.653799e-01 0.000e+00 2.788e+00 5.723e-02 6 30 1.897216e-01 0.000e+00 2.311e+00 1.147e-01 7 33 1.513701e-01 0.000e+00 9.706e-01 5.764e-02 8 36 1.153330e-01 0.000e+00 1.127e+00 8.169e-02 9 39 1.198058e-01 0.000e+00 1.000e-01 1.522e-02 10 42 8.910052e-02 0.000e+00 8.378e-01 8.301e-02 11 45 6.771960e-02 0.000e+00 1.365e+00 7.149e-02 12 48 6.437664e-02 0.000e+00 1.146e-01 5.701e-03 13 51 6.329037e-02 0.000e+00 1.883e-02 3.774e-03 14 54 5.161934e-02 0.000e+00 3.016e-01 4.464e-02 15 57 4.964194e-02 0.000e+00 7.913e-02 7.894e-03 16 60 4.955404e-02 0.000e+00 5.462e-03 4.185e-04 17 63 4.954839e-02 0.000e+00 3.993e-03 2.208e-05 18 66 4.658289e-02 0.000e+00 1.318e-02 1.255e-02 19 69 4.647011e-02 0.000e+00 8.006e-04 4.940e-04 20 72 4.569141e-02 0.000e+00 3.136e-03 3.379e-03 21 75 4.568281e-02 0.000e+00 6.437e-05 3.974e-05 22 78 4.568281e-02 0.000e+00 8.000e-06 1.083e-07 23 81 4.567641e-02 0.000e+00 1.601e-06 2.793e-05 24 84 4.567482e-02 0.000e+00 1.996e-08 6.916e-06
Your table can differ, depending on toolbox version and computing platform. The following description applies to the table as displayed.
The first column, labeled
is the iteration number from 0 to 24.
24 iterations to converge.
The second column, labeled
reports the cumulative number of times Rosenbrock's function was evaluated.
The final row shows an
F-count of 84, indicating
fmincon evaluated Rosenbrock's function
84 times in the process of finding a minimum.
The third column, labeled
f(x), displays the value of the objective
function. The final value, 0.04567482, is the minimum reported in the
Optimization app Run solver and view results box, and
at the end of the exit message in the command window.
The fourth column,
Feasibility, is 0 for all iterations. This column shows
the value of the constraint function
unitdisk at each
iteration where the constraint is positive. Because the value of
unitdisk was negative in all iterations, every
iteration satisfied the constraint.
The other columns of the iteration table are described in Iterative Display.