OSQP Solver object
OSQP Solver object
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. |
q, l, u |
Numeric vectors, with possibly infinite elements in l and u |
pars |
list with optimization parameters, conveniently set with the function
|
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.
An R6-object of class "osqp_model" with methods defined which can be further used to solve the problem with updated settings / parameters.
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$WarmStart(x=NULL, y=NULL)
print(model)a string with the name of one of the matrices / vectors of the problem
list with optimization parameters
## example, adapted from OSQP documentation
library(Matrix)
P <- Matrix(c(11., 0.,
0., 0.), 2, 2, sparse = TRUE)
q <- c(3., 4.)
A <- Matrix(c(-1., 0., -1., 2., 3.,
0., -1., -3., 5., 4.)
, 5, 2, sparse = TRUE)
u <- c(0., 0., -15., 100., 80)
l <- rep_len(-Inf, 5)
settings <- osqpSettings(verbose = FALSE)
model <- osqp(P, q, A, l, u, settings)
# Solve
res <- model$Solve()
# Define new vector
q_new <- c(10., 20.)
# Update model and solve again
model$Update(q = q_new)
res <- model$Solve()Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.