Solve a DCP Problem
Solve a DCP compliant optimization problem.
psolve( object, solver = NA, ignore_dcp = FALSE, warm_start = FALSE, verbose = FALSE, parallel = FALSE, gp = FALSE, feastol = NULL, reltol = NULL, abstol = NULL, num_iter = NULL, ... ) ## S4 method for signature 'Problem' psolve( object, solver = NA, ignore_dcp = FALSE, warm_start = FALSE, verbose = FALSE, parallel = FALSE, gp = FALSE, feastol = NULL, reltol = NULL, abstol = NULL, num_iter = NULL, ... ) ## S4 method for signature 'Problem,ANY' solve(a, b = NA, ...)
object, a |
A Problem object. |
solver, b |
(Optional) A string indicating the solver to use. Defaults to "ECOS". |
ignore_dcp |
(Optional) A logical value indicating whether to override the DCP check for a problem. |
warm_start |
(Optional) A logical value indicating whether the previous solver result should be used to warm start. |
verbose |
(Optional) A logical value indicating whether to print additional solver output. |
parallel |
(Optional) A logical value indicating whether to solve in parallel if the problem is separable. |
gp |
(Optional) A logical value indicating whether the problem is a geometric program. Defaults to |
feastol |
The feasible tolerance on the primal and dual residual. |
reltol |
The relative tolerance on the duality gap. |
abstol |
The absolute tolerance on the duality gap. |
num_iter |
The maximum number of iterations. |
... |
Additional options that will be passed to the specific solver. In general, these options will override any default settings imposed by CVXR. |
A list containing the solution to the problem:
statusThe status of the solution. Can be "optimal", "optimal_inaccurate", "infeasible", "infeasible_inaccurate", "unbounded", "unbounded_inaccurate", or "solver_error".
valueThe optimal value of the objective function.
solverThe name of the solver.
solve_timeThe time (in seconds) it took for the solver to solve the problem.
setup_timeThe time (in seconds) it took for the solver to set up the problem.
num_itersThe number of iterations the solver had to go through to find a solution.
getValueA function that takes a Variable object and retrieves its primal value.
getDualValueA function that takes a Constraint object and retrieves its dual value(s).
a <- Variable(name = "a") prob <- Problem(Minimize(norm_inf(a)), list(a >= 2)) result <- psolve(prob, solver = "ECOS", verbose = TRUE) result$status result$value result$getValue(a) result$getDualValue(constraints(prob)[[1]])
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