R/179_reductions_solution.R

In CVXR: Disciplined Convex Optimization

#####
## DO NOT EDIT THIS FILE!! EDIT THE SOURCE INSTEAD: rsrc_tree/reductions/solution.R
#####

## CVXPY SOURCE: reductions/solution.py
## Solution -- holds solver result for inversion through the reduction chain


# -- Solution class ------------------------------------------------
## CVXPY SOURCE: solution.py lines 56-94

Solution <- new_class("Solution", package = "CVXR",
  properties = list(
    status      = class_character,
    opt_val     = class_any,        # numeric or NA_real_
    primal_vars = class_list,       # named list: as.character(var_id) -> matrix
    dual_vars   = class_list,       # named list: as.character(constr_id) -> matrix
    attr        = class_list        # misc solver info
  ),
  constructor = function(status, opt_val = NA_real_,
                         primal_vars = list(), dual_vars = list(),
                         attr = list()) {
    new_object(S7_object(),
      status      = status,
      opt_val     = opt_val,
      primal_vars = primal_vars,
      dual_vars   = dual_vars,
      attr        = attr
    )
  }
)

# -- print --------------------------------------------------------

method(print, Solution) <- function(x, ...) {
  cat(sprintf("Solution(status=%s, opt_val=%s, %d primal, %d dual)\n",
    x@status,
    if (is.na(x@opt_val)) "NA" else format(x@opt_val, digits = 6),
    length(x@primal_vars),
    length(x@dual_vars)))
  invisible(x)
}

# -- failure_solution: factory for infeasible/unbounded -----------
## CVXPY SOURCE: solution.py lines 30-53

failure_solution <- function(status, attr = list()) {
  if (status %in% c(INFEASIBLE, INFEASIBLE_INACCURATE)) {
    opt_val <- Inf
  } else if (status %in% c(UNBOUNDED, UNBOUNDED_INACCURATE)) {
    opt_val <- -Inf
  } else {
    opt_val <- NA_real_
  }
  if (status == INFEASIBLE_OR_UNBOUNDED) {
    attr[["message"]] <- paste(
      "The problem is either infeasible or unbounded, but the solver",
      "cannot tell which. Disable any solver-specific presolve methods",
      "and re-solve to determine the precise problem status.")
  }
  Solution(status = status, opt_val = opt_val, primal_vars = list(),
           dual_vars = list(), attr = attr)
}