Sparse Quadratic Programming Solver
Solves
argmin_x 0.5 x'P x + q'x
s.t.
li < (A x)i < ui
for real matrices P (nxn, positive semidefinite) and A (mxn) with m number of constraints
solve_osqp(P = NULL, q = NULL, A = NULL, l = NULL, u = NULL, pars = osqpSettings())
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 |
A list with elements x (the primal solution), y (the dual solution), prim_inf_cert, dual_inf_cert, and info.
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.
osqp. The underlying OSQP documentation: https://osqp.org/
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$xPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.