| Title: | Quadratic Programming Solver using the 'OSQP' Library |
| Version: | 0.6.3.3 |
| Date: | 2024-06-07 |
| Copyright: | file COPYRIGHT |
| Description: | Provides bindings to the 'OSQP' solver. The 'OSQP' solver is a numerical optimization package or solving convex quadratic programs written in 'C' and based on the alternating direction method of multipliers. See <doi:10.48550/arXiv.1711.08013> for details. |
| License: | Apache License 2.0 | file LICENSE |
| SystemRequirements: | C++17 |
| Imports: | Rcpp (≥ 0.12.14), methods, Matrix (≥ 1.6.1), R6 |
| LinkingTo: | Rcpp |
| RoxygenNote: | 7.2.3 |
| Collate: | 'RcppExports.R' 'osqp-package.R' 'sparse.R' 'solve.R' 'osqp.R' 'params.R' |
| NeedsCompilation: | yes |
| Suggests: | slam, testthat |
| Encoding: | UTF-8 |
| BugReports: | https://github.com/osqp/osqp-r/issues |
| URL: | https://osqp.org |
| Packaged: | 2024-06-08 03:42:50 UTC; naras |
| Author: | Bartolomeo Stellato [aut, ctb, cph], Goran Banjac [aut, ctb, cph], Paul Goulart [aut, ctb, cph], Stephen Boyd [aut, ctb, cph], Eric Anderson [ctb], Vineet Bansal [aut, ctb], Balasubramanian Narasimhan [cre, ctb] |
| Maintainer: | Balasubramanian Narasimhan <naras@stanford.edu> |
| Repository: | CRAN |
| Date/Publication: | 2024-06-08 05:30:01 UTC |
OSQP Solver object
Description
OSQP Solver object
Usage
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. (In the interest of efficiency, only the upper triangular part of P is used) |
q, l, u |
Numeric vectors, with possibly infinite elements in l and u |
pars |
list with optimization parameters, conveniently set with the function
|
Details
Allows one to solve a parametric
problem with for example warm starts between updates of the parameter, c.f. the examples.
The object returned by osqp contains several methods which can be used to either update/get details of the
problem, modify the optimization settings or attempt to solve the problem.
Value
An R6-object of class "osqp_model" with methods defined which can be further used to solve the problem with updated settings / parameters.
Usage
model = osqp(P=NULL, q=NULL, A=NULL, l=NULL, u=NULL, pars=osqpSettings())
model$Solve()
model$Update(q = NULL, l = NULL, u = NULL, Px = NULL, Px_idx = NULL, Ax = NULL, Ax_idx = NULL)
model$GetParams()
model$GetDims()
model$UpdateSettings(newPars = list())
model$GetData(element = c("P", "q", "A", "l", "u"))
model$WarmStart(x=NULL, y=NULL)
print(model)
Method Arguments
- element
a string with the name of one of the matrices / vectors of the problem
- newPars
list with optimization parameters
See Also
Examples
## example, adapted from OSQP documentation
library(Matrix)
P <- Matrix(c(11., 0.,
0., 0.), 2, 2, sparse = TRUE)
q <- c(3., 4.)
A <- Matrix(c(-1., 0., -1., 2., 3.,
0., -1., -3., 5., 4.)
, 5, 2, sparse = TRUE)
u <- c(0., 0., -15., 100., 80)
l <- rep_len(-Inf, 5)
settings <- osqpSettings(verbose = FALSE)
model <- osqp(P, q, A, l, u, settings)
# Solve
res <- model$Solve()
# Define new vector
q_new <- c(10., 20.)
# Update model and solve again
model$Update(q = q_new)
res <- model$Solve()
Settings for OSQP
Description
For further details please consult the OSQP documentation: https://osqp.org/
Usage
osqpSettings(
rho = 0.1,
sigma = 1e-06,
max_iter = 4000L,
eps_abs = 0.001,
eps_rel = 0.001,
eps_prim_inf = 1e-04,
eps_dual_inf = 1e-04,
alpha = 1.6,
linsys_solver = c(QDLDL_SOLVER = 0L),
delta = 1e-06,
polish = FALSE,
polish_refine_iter = 3L,
verbose = TRUE,
scaled_termination = FALSE,
check_termination = 25L,
warm_start = TRUE,
scaling = 10L,
adaptive_rho = 1L,
adaptive_rho_interval = 0L,
adaptive_rho_tolerance = 5,
adaptive_rho_fraction = 0.4,
time_limit = 0
)
Arguments
rho |
ADMM step rho |
sigma |
ADMM step sigma |
max_iter |
maximum iterations |
eps_abs |
absolute convergence tolerance |
eps_rel |
relative convergence tolerance |
eps_prim_inf |
primal infeasibility tolerance |
eps_dual_inf |
dual infeasibility tolerance |
alpha |
relaxation parameter |
linsys_solver |
which linear systems solver to use, 0=QDLDL, 1=MKL Pardiso |
delta |
regularization parameter for polish |
polish |
boolean, polish ADMM solution |
polish_refine_iter |
iterative refinement steps in polish |
verbose |
boolean, write out progress |
scaled_termination |
boolean, use scaled termination criteria |
check_termination |
integer, check termination interval. If 0, termination checking is disabled |
warm_start |
boolean, warm start |
scaling |
heuristic data scaling iterations. If 0, scaling disabled |
adaptive_rho |
cboolean, is rho step size adaptive? |
adaptive_rho_interval |
Number of iterations between rho adaptations rho. If 0, it is automatic |
adaptive_rho_tolerance |
Tolerance X for adapting rho. The new rho has to be X times larger or 1/X times smaller than the current one to trigger a new factorization |
adaptive_rho_fraction |
Interval for adapting rho (fraction of the setup time) |
time_limit |
run time limit with 0 indicating no limit |
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 |
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