solve_osqp: Sparse Quadratic Programming Solver

In osqp: Quadratic Programming Solver using the 'OSQP' Library

Sparse Quadratic Programming Solver

Description

Solves

arg\min_x 0.5 x'P x + q'x

s.t.

l_i < (A x)_i < u_i

for real matrices P (nxn, positive semidefinite) and A (mxn) with m number of constraints

Usage

solve_osqp(
  P = NULL,
  q = NULL,
  A = NULL,
  l = NULL,
  u = NULL,
  pars = osqpSettings()
)

Arguments

P, A	sparse matrices of class dgCMatrix or coercible into such, with P positive semidefinite. Only the upper triangular part of P will be used.
q, l, u	Numeric vectors, with possibly infinite elements in l and u
pars	list with optimization parameters, conveniently set with the function osqpSettings

Value

A list with elements x (the primal solution), y (the dual solution), prim_inf_cert, dual_inf_cert, and info.

References

Stellato, B., Banjac, G., Goulart, P., Bemporad, A., Boyd and S. (2018). “OSQP: An Operator Splitting Solver for Quadratic Programs.” ArXiv e-prints. 1711.08013.

See Also

osqp. The underlying OSQP documentation: https://osqp.org/

Examples

library(osqp)
## example, adapted from OSQP documentation
library(Matrix)

P <- Matrix(c(11., 0.,
              0., 0.), 2, 2, sparse = TRUE)
q <- c(3., 4.)
A <- Matrix(c(-1., 0., -1., 2., 3.,
              0., -1., -3., 5., 4.)
              , 5, 2, sparse = TRUE)
u <- c(0., 0., -15., 100., 80)
l <- rep_len(-Inf, 5)

settings <- osqpSettings(verbose = TRUE)

# Solve with OSQP
res <- solve_osqp(P, q, A, l, u, settings)
res$x

osqp documentation built on June 22, 2024, 10:20 a.m.