Explicit API

Internals of the explicit predictor–corrector backend. Import directly from qpax.explicit only when you specifically want this backend's implementation.

qpax.explicit.pdip.solve_qp

solve_qp(Q: Array, q: Array, A: Array, b: Array, G: Array, h: Array, solver_tol: float = 1e-05, max_iter: int = 30, linear_solver: LinearSolver = LinearSolver.CHOLESKY, return_bad_step: bool = False, sigma: float = 0.125, verbose: bool = False)

Solve a QP using a primal-dual interior point method.

Parameters:

Name	Type	Description	Default
Q	Array	(n, n) positive definite matrix	required
q	Array	(n,) vector	required
A	Array	(m, n) equality constraint matrix	required
b	Array	(m,) equality constraint vector	required
G	Array	(p, n) inequality constraint matrix	required
h	Array	(p,) inequality constraint vector	required
linear_solver	LinearSolver	LinearSolver.CHOLESKY or LinearSolver.QR	CHOLESKY

Returns:

Name	Type	Description
x		(n,) optimal solution
s		(p,) inequality slack variables
z		(p,) inequality dual variables
y		(m,) equality dual variables
converged		int convergence flag
pdip_iter		int number of iterations

qpax.explicit.diff_qp.solve_qp_primal

solve_qp_primal(Q, q, A, b, G, h, solver_tol=1e-05, target_kappa=0.001, max_iter=30)

qpax.explicit.elastic_qp.solve_qp_elastic

solve_qp_elastic(Q, q, G, h, penalty, solver_tol=0.001, max_iter=30)

qpax.explicit.elastic_qp.solve_qp_elastic_primal

solve_qp_elastic_primal(Q, q, G, h, penalty, solver_tol=1e-05, target_kappa=0.001, max_iter=30)

qpax.explicit.pdip_relaxed.relax_qp

relax_qp(Q, q, A, b, G, h, x, s, z, y, solver_tol=1e-05, target_kappa=0.001, max_iter=30, return_bad_step=False, sigma: float = 0.125, verbose: bool = False)