Crate osqp

The OSQP (Operator Splitting Quadratic Program) solver is a numerical optimization package for solving convex quadratic programs in the form

[\begin{split}\begin{array}{ll} \mbox{minimize} & \frac{1}{2} x^T P x + q^T x \ \mbox{subject to} & l \leq A x \leq u \end{array}\end{split}]

where (x) is the optimization variable and (P \in \mathbf{S}^{n}_{+}) a positive semidefinite matrix.

Further information about the solver is available at osqp.readthedocs.io.

Example

Consider the following QP

\[\begin{split}\begin{array}{ll} \mbox{minimize} & \frac{1}{2} x^T \begin{bmatrix}4 & 1\\ 1 & 2 \end{bmatrix} x + \begin{bmatrix}1 \\ 1\end{bmatrix}^T x \\ \mbox{subject to} & \begin{bmatrix}1 \\ 0 \\ 0\end{bmatrix} \leq \begin{bmatrix} 1 & 1\\ 1 & 0\\ 0 & 1\end{bmatrix} x \leq \begin{bmatrix}1 \\ 0.7 \\ 0.7\end{bmatrix} \end{array}\end{split}\]

use osqp::{Settings, Problem};

// Define problem data
let P = &[[4.0, 1.0],
          [1.0, 2.0]];
let q = &[1.0, 1.0];
let A = &[[1.0, 1.0],
          [1.0, 0.0],
          [0.0, 1.0]];
let l = &[1.0, 0.0, 0.0];
let u = &[1.0, 0.7, 0.7];

// Change the default alpha and disable verbose output
let settings = Settings::default()
    .alpha(1.0)
    .verbose(false);

// Create an OSQP problem
let mut prob = Problem::new(P, q, A, l, u, &settings);

// Solve problem
let result = prob.solve();

// Print the solution
println!("{:?}", result.x().expect("failed to solve problem"));

Structs

CscMatrix	A matrix in Compressed Sparse Column format.
DualInfeasibilityCertificate	A proof of dual infeasibility.
PrimalInfeasibilityCertificate	A proof of primal infeasibility.
Problem	An instance of the OSQP solver.
Settings	The settings used when initialising a solver.
Solution	A solution to a problem.

Enums

LinsysSolver	The linear system solver for OSQP to use.
PolishStatus	The status of the polish operation.
Status	The result of solving a problem.