Creates a persistent solver that can be reused across multiple solves with updated problem data (warm starts). This avoids the overhead of reallocating the solver's internal data structures when only the problem data changes but the sparsity pattern stays the same.
Usage
clarabel_solver(
A,
b,
q,
P = NULL,
cones,
control = list(),
strict_cone_order = TRUE
)Arguments
- A
a matrix of constraint coefficients.
- b
a numeric vector giving the primal constraints
- q
a numeric vector giving the primal objective
- P
a symmetric positive semidefinite matrix, default
NULL- cones
a named list giving the cone sizes, see “Cone Parameters” below for specification
- control
a list giving specific control parameters to use in place of default values, with an empty list indicating the default control parameters. Specified parameters should be correctly named and typed to avoid Rust system panics as no sanitization is done for efficiency reasons
- strict_cone_order
a logical flag, default
TRUEfor forcing order of cones described below. IfFALSEcones can be specified in any order and even repeated and directly passed to the solver without type and length checks
Value
a ClarabelSolver environment object with methods
solve(), update_data(Px, Ax, q, b), and
is_update_allowed()
Details
Data updates are refused once the solver has altered the structure of
the problem, since the stored factorization no longer corresponds to
the data being updated. Three settings can cause that: presolve_enable,
input_sparse_dropzeros and chordal_decomposition_enable. What
matters is whether the transformation actually took place, not whether
it was permitted: presolve rewrites the problem only if it finds
something to remove, and chordal decomposition engages only for
semidefinite constraints whose sparsity pattern is decomposable. Use
solver_is_update_allowed() to check the instance you built.
Examples
if (FALSE) { # \dontrun{
P <- Matrix::sparseMatrix(i = 1:2, j = 1:2, x = c(2, 1), dims = c(2, 2))
A <- matrix(c(1, 0, 0, 1), nrow = 2)
b <- c(1, 1)
q <- c(-2, -3)
cones <- list(l = 2L)
ctrl <- clarabel_control(presolve_enable = FALSE, verbose = FALSE)
s <- clarabel_solver(A, b, q, P, cones, control = ctrl)
sol1 <- solver_solve(s)
solver_update(s, q = c(-4, -1))
sol2 <- solver_solve(s)
} # }