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OSQP Solver object

Description

OSQP Solver object

Usage

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

Arguments

Details

Allows one to solve a parametricproblem with for example warm starts between updates of the parameter, c.f. the examples.The object returned byosqp contains several methods which can be used to either update/get details of theproblem, modify the optimization settings or attempt to solve the problem.

Value

An R6-object of class "osqp_model" with methods defined which can be furtherused 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

solve_osqp

Examples

## example, adapted from OSQP documentationlibrary(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)# Solveres <- model$Solve()# Define new vectorq_new <- c(10., 20.)# Update model and solve againmodel$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

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

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 documentationlibrary(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 OSQPres <- solve_osqp(P, q, A, l, u, settings)res$x