secondordercone

Create second-order cone constraint

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Syntax

socConstraint = secondordercone(A,b,d,gamma)

Description

The secondordercone function creates a second-order cone constraint representing the inequality

‖A⋅x−b‖≤dT⋅x−γ

from the input matrices A, b, d, and gamma.

example

socConstraint = secondordercone(A,b,d,gamma) creates a second-order cone constraint object socConstraint.

Solve problems with second-order cone constraints by using the coneprog function. To represent multiple cone constraints, pass an array of these constraints to coneprog as shown in the example Several Cone Constraints.

Examples

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Open Live Script

To set up a problem with a second-order cone constraint, create a second-order cone constraint object.

A = diag([1,1/2,0]);
b = zeros(3,1);
d = [0;0;1];
gamma = 0;
socConstraints = secondordercone(A,b,d,gamma);

Create an objective function vector.

f = [-1,-2,0];

The problem has no linear constraints. Create empty matrices for these constraints.

Aineq = [];
bineq = [];
Aeq = [];
beq = [];

Set upper and lower bounds on x(3).

lb = [-Inf,-Inf,0];
ub = [Inf,Inf,2];

Solve the problem by using the coneprog function.

[x,fval] = coneprog(f,socConstraints,Aineq,bineq,Aeq,beq,lb,ub)
Optimal solution found.

x = 3×1

    0.4851
    3.8806
    2.0000


fval = -8.2462

The solution component x(3) is at its upper bound. The cone constraint is active at the solution:

norm(A*x-b) - d'*x % Near 0 when the constraint is active
ans = -2.5677e-08
Open Live Script

To set up a problem with several second-order cone constraints, create an array of constraint objects. To save time and memory, create the highest-index constraint first.

A = diag([1,2,0]);
b = zeros(3,1);
d = [0;0;1];
gamma = -1;
socConstraints(3) = secondordercone(A,b,d,gamma);

A = diag([3,0,1]);
d = [0;1;0];
socConstraints(2) = secondordercone(A,b,d,gamma);

A = diag([0;1/2;1/2]);
d = [1;0;0];
socConstraints(1) = secondordercone(A,b,d,gamma);

Create the linear objective function vector.

f = [-1;-2;-4];

Solve the problem by using the coneprog function.

[x,fval] = coneprog(f,socConstraints)
Optimal solution found.

x = 3×1

    0.4238
    1.6477
    2.3225


fval = -13.0089

Input Arguments

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Linear factor of the cone, specified as a real matrix. The number of columns in A must equal the number of elements in d, and the number of rows in A must equal the number of elements in b.

Example: diag([1,1/2,0])

Data Types: double

Center of the cone, specified as a real vector. The number of elements in b must equal the number of rows in A.

Example: zeros(3,1)

Data Types: double

Linear bound, specified as a real vector. The number of elements in d must equal the number of columns in A.

Example: [0;0;1]

Data Types: double

Bound, specified as a real scalar. Smaller values of gamma correspond to looser constraints.

Example: -1

Data Types: double

Output Arguments

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Second-order cone constraint, returned as a SecondOrderConeConstraint object. Use this object as a constraint for the coneprog solver. If you have multiple cone constraints, pass a vector of constraints to coneprog; see Several Cone Constraints.

See Also

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Topics

Introduced in R2020b

Optimization Toolbox Documentation

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