Convert Constraints in `for` Loops for Static Analysis

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Static analysis increases the speed of for loops in problem-based expressions, as described in Static Analysis of Optimization Expressions. Static analysis applies to expressions created by fcn2optimexpr, not to the comparison operators <=, ==, and >=. Therefore, when you have a constraint created using a for loop, subtract the right side of the constraint from both sides of the comparison so that the comparison is to zero, or to a constant. Then apply fcn2optimexpr.

This example has the constraints $x_{i} \leq u + i - 1$ and $x_{i} \geq - u - i + 1$ for $i = 1, 2, . . ., 20$ . Begin by creating optimization variables.

N = 20;
x = optimvar("x",N);
u = optimvar("u");

To express the constraints in a for loop, subtract the appropriate values so that the constraints are compared to 0:

$\begin{array}{c} x_{i} - u - i + 1 \leq 0 \\ x_{i} + u + i - 1 \geq 0 . \end{array}$

Typically, you express these constraints in the following code:

for i = 1:N
    cons1(i) = x(i) - u - i + 1;
    cons2(i) = x(i) + u + i - 1;
end

To apply static analysis, place this for loop in a separate helper function named forloopfcn. The resulting function appears at the end of this example. (You can easily convert a for loop in your script to a separate function, as shown in Create for Loop for Static Analysis.)

To take advantage of static analysis, convert the function to an optimization expression using fcn2optimexpr.

[cons1,cons2] = fcn2optimexpr(@forloopfcn,x,u);

Create an optimization problem and place the constraints in the problem.

prob = optimproblem;
prob.Constraints.cons1 = cons1 <= 0;
prob.Constraints.cons2 = cons2 >= 0;

Create an objective function that is a weighted sum of the u variable and the sum of squared differences of the x variables from the vector 1.5*(1:N).

M = 100; % Weight factor for u
prob.Objective = M*u + sum((x - 1.5*(1:N)').^2);

Review the problem.

show(prob)

  OptimizationProblem : 

	Solve for:
       u, x

	minimize :
       x(1)^2 + x(2)^2 + x(3)^2 + x(4)^2 + x(5)^2 + x(6)^2 + x(7)^2 + x(8)^2 + x(9)^2 + x(10)^2 + x(11)^2 + x(12)^2 + x(13)^2 + x(14)^2 + x(15)^2 + x(16)^2 + x(17)^2 + x(18)^2 + x(19)^2 + x(20)^2 + 100*u - 3*x(1) - 6*x(2) - 9*x(3) - 12*x(4) - 15*x(5) - 18*x(6) - 21*x(7) - 24*x(8) - 27*x(9) - 30*x(10) - 33*x(11) - 36*x(12) - 39*x(13) - 42*x(14) - 45*x(15) - 48*x(16) - 51*x(17) - 54*x(18) - 57*x(19) - 60*x(20) + 6457.5


	subject to cons1:
       -u + x(1) <= 0
       -u + x(2) <= 1
       -u + x(3) <= 2
       -u + x(4) <= 3
       -u + x(5) <= 4
       -u + x(6) <= 5
       -u + x(7) <= 6
       -u + x(8) <= 7
       -u + x(9) <= 8
       -u + x(10) <= 9
       -u + x(11) <= 10
       -u + x(12) <= 11
       -u + x(13) <= 12
       -u + x(14) <= 13
       -u + x(15) <= 14
       -u + x(16) <= 15
       -u + x(17) <= 16
       -u + x(18) <= 17
       -u + x(19) <= 18
       -u + x(20) <= 19

	subject to cons2:
       u + x(1) >= 0
       u + x(2) >= -1
       u + x(3) >= -2
       u + x(4) >= -3
       u + x(5) >= -4
       u + x(6) >= -5
       u + x(7) >= -6
       u + x(8) >= -7
       u + x(9) >= -8
       u + x(10) >= -9
       u + x(11) >= -10
       u + x(12) >= -11
       u + x(13) >= -12
       u + x(14) >= -13
       u + x(15) >= -14
       u + x(16) >= -15
       u + x(17) >= -16
       u + x(18) >= -17
       u + x(19) >= -18
       u + x(20) >= -19

Solve the problem.

[sol,fval,exitflag,output] = solve(prob)

Solving problem using quadprog.

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.

<stopping criteria details>

sol = struct with fields:
    u: 4.1786
    x: [20×1 double]

fval = 653.3036

exitflag = 
    OptimalSolution

output = struct with fields:
            message: '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.↵↵<stopping criteria details>↵↵Optimization completed: The relative dual feasibility, 1.421085e-16,↵is less than options.OptimalityTolerance = 1.000000e-08, the complementarity measure,↵1.536130e-10, is less than options.OptimalityTolerance, and the relative maximum constraint↵violation, 0.000000e+00, is less than options.ConstraintTolerance = 1.000000e-08.'
          algorithm: 'interior-point-convex'
      firstorderopt: 6.1445e-09
    constrviolation: 0
         iterations: 8
       linearsolver: 'sparse'
       cgiterations: []
             solver: 'quadprog'

View the x variables in the solution.

disp(sol.x)

View the constraint function values at the solution.

evaluate(cons1,sol)

ans = 1×20

   -2.6786   -2.1786   -1.6786   -1.1786   -0.6786   -0.1786   -0.0000   -0.0000   -0.0000   -0.0000   -0.0000   -0.0000   -0.0000   -0.0000   -0.0000   -0.0000   -0.0000   -0.0000   -0.0000   -0.0000

evaluate(cons2,sol)

ans = 1×20

    5.6786    8.1786   10.6786   13.1786   15.6786   18.1786   20.3571   22.3571   24.3571   26.3571   28.3571   30.3571   32.3571   34.3571   36.3571   38.3571   40.3571   42.3571   44.3571   46.3571

The cons1 constraint specifies that the value is nonpositive. The first six values of cons1 are negative, and the remaining values are zero to display precision. Therefore, at the solution, the cons1 constraint is active for all values except the first six values. In contrast, the cons2 constraint specifies that the value is nonnegative. All values of cons2 are strictly positive, meaning this constraint is not active at the solution.

Helper Function

This code creates the forloopfcn helper function.

function [cons1,cons2] = forloopfcn(x,u)
N = length(x);
cons1 = zeros(1,N);
cons2 = zeros(1,N);
for i = 1:N
    cons1(i) = x(i) - u - i + 1;
    cons2(i) = x(i) + u + i - 1;
end
end

Convert Constraints in `for` Loops for Static Analysis

Helper Function

See Also

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Convert Constraints in for Loops for Static Analysis

Helper Function

See Also

Related Topics

Convert Constraints in `for` Loops for Static Analysis