How to Use All Types of Constraints

This example is a nonlinear minimization problem with all possible types of constraints. The example does not use gradients.

The problem has five variables, x(1) through x(5). The objective function is a polynomial in the variables.

$$f(x)=6x_2{x}_5+7x_1{x}_3+3x_2^2$$.

The objective function is in the local function myobj(x), which is nested inside the function fullexample. The code for fullexample appears at the end of this example.

The nonlinear constraints are likewise polynomial expressions.

$$x_1-0.2x_2{x}_5\le 71$$

$$0.9x_3-x_4^2\le 67$$

$$3x_2^2{x}_5+3x_1^2{x}_3=20.875$$.

The nonlinear constraints are in the local function myconstr(x), which is nested inside the function fullexample.

The problem has bounds on $$x_3$$ and $$x_5$$.

$$0\le x_3\le 20$$, $$x_5\ge 1$$.

The problem has a linear equality constraint $$x_1=0.3x_2$$, which you can write as $$x_1-0.3x_2=0$$.

The problem also has three linear inequality constraints:

$$\begin{array}{c}0.1x_5\le x_4\\ x_4\le 0.5x_5\\ 0.9x_5\le x_3.\end{array}$$

Represent the bounds and linear constraints as matrices and vectors. The code that creates these arrays is in the fullexample function. As described in the fmincon Input Arguments section, the lb and ub vectors represent the constraints

lb $$\le x\le$$ ub.

The matrix A and vector b represent the linear inequality constraints

A*x $$\le$$ b,

and the matrix Aeq and vector beq represent the linear equality constraints

Aeq*x = b.

Call fullexample to solve the minimization problem subject to all types of constraints.

[x,fval,exitflag] = fullexample

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.

x = 5×1

    0.6114
    2.0380
    1.3948
    0.1572
    1.5498

fval = 
37.3806

exitflag = 
1

The exit flag value of 1 indicates that fmincon converges to a local minimum that satisfies all of the constraints.

This code creates the fullexample function, which contains the nested functions myobj and myconstr.

function [x,fval,exitflag] = fullexample
x0 = [1; 4; 5; 2; 5];
lb = [-Inf; -Inf;  0; -Inf;   1];
ub = [ Inf;  Inf; 20; Inf; Inf];
Aeq = [1 -0.3 0 0 0];
beq = 0;
A = [0 0  0 -1  0.1
     0 0  0  1 -0.5
     0 0 -1  0  0.9];
b = [0; 0; 0];
opts = optimoptions(@fmincon,'Algorithm','sqp');
     
[x,fval,exitflag] = fmincon(@myobj,x0,A,b,Aeq,beq,lb,ub,...
                                  @myconstr,opts);

%---------------------------------------------------------
function f = myobj(x)

f = 6*x(2)*x(5) + 7*x(1)*x(3) + 3*x(2)^2;
end

%---------------------------------------------------------
function [c, ceq] = myconstr(x)

c = [x(1) - 0.2*x(2)*x(5) - 71
     0.9*x(3) - x(4)^2 - 67];
ceq = 3*x(2)^2*x(5) + 3*x(1)^2*x(3) - 20.875;
end
end

Related Topics

• Write Constraints
• Solver-Based Nonlinear Optimization