Solve a DCP Problem — psolve • CVXR

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 class '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 class 'Problem,ANY'
solve(a, b = NA, ...)

Arguments

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 FALSE.
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.

Value

A list containing the solution to the problem:

status: The status of the solution. Can be "optimal", "optimal_inaccurate", "infeasible", "infeasible_inaccurate", "unbounded", "unbounded_inaccurate", or "solver_error".
value: The optimal value of the objective function.
solver: The name of the solver.
solve_time: The time (in seconds) it took for the solver to solve the problem.
setup_time: The time (in seconds) it took for the solver to set up the problem.
num_iters: The number of iterations the solver had to go through to find a solution.
getValue: A function that takes a Variable object and retrieves its primal value.
getDualValue: A function that takes a Constraint object and retrieves its dual value(s).

Examples

a <- Variable(name = "a")
prob <- Problem(Minimize(norm_inf(a)), list(a >= 2))
result <- psolve(prob, solver = "ECOS", verbose = TRUE)
result$status
#> [1] "optimal"
result$value
#> [1] 2
result$getValue(a)
#> [1] 2
result$getDualValue(constraints(prob)[[1]])
#> [1] 1