osqp: OSQP Solver object

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

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 osqpSettings. For osqpObject$UpdateSettings(newPars) only a subset of the settings can be updated once the problem has been initialized.

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

solve_osqp

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()

osqp documentation built on Oct. 20, 2023, 5:12 p.m.