add_cuts_portfolio.R

#' @include Portfolio-proto.R
NULL

#' Add Bender's cuts portfolio
#'
#' Generate a portfolio of solutions for a conservation planning
#' [problem()] using Bender's cuts (discussed in Rodrigues
#' *et al.* 2000). This is recommended as a replacement for
#'  [add_gap_portfolio()] when the *Gurobi* software is not
#'  available.
#'
#' @param x [problem()] (i.e., [`ConservationProblem-class`]) object.
#'
#' @param number_solutions `integer` number of attempts to generate
#'   different solutions. Defaults to 10.
#'
#' @details This strategy for generating a portfolio of solutions involves
#'   solving the problem multiple times and adding additional constraints
#'   to forbid previously obtained solutions. In general, this strategy is most
#'   useful when problems take a long time to solve and benefit from
#'   having multiple threads allocated for solving an individual problem.
#
#' @section Notes:
#' In early versions (< 4.0.1), this function was only compatible with
#' *Gurobi* (i.e., [add_gurobi_solver()]). To provide functionality with
#' exact algorithm solvers, this function now adds constraints to the
#' problem formulation to generate multiple solutions.
#'
#' @return Object (i.e., [`ConservationProblem-class`]) with the portfolio
#'  added to it.
#'
#' @seealso
#' See [portfolios] for an overview of all functions for adding a portfolio.
#'
#' @family portfolios
#'
#' @references
#' Rodrigues AS, Cerdeira OJ, and Gaston KJ (2000) Flexibility,
#' efficiency, and accountability: adapting reserve selection algorithms to
#' more complex conservation problems. *Ecography*, 23: 565--574.
#'
#' @examples
#' # set seed for reproducibility
#' set.seed(500)
#'
#' # load data
#' data(sim_pu_raster, sim_features, sim_pu_zones_stack, sim_features_zones)
#'
#' # create minimal problem with cuts portfolio
#' p1 <- problem(sim_pu_raster, sim_features) %>%
#'       add_min_set_objective() %>%
#'       add_relative_targets(0.2) %>%
#'       add_cuts_portfolio(10) %>%
#'       add_default_solver(gap = 0.2, verbose = FALSE)
#'
#' \dontrun{
#' # solve problem and generate 10 solutions within 20% of optimality
#' s1 <- solve(p1)
#'
#' # plot solutions in portfolio
#' plot(stack(s1), axes = FALSE, box = FALSE)
#' }
#' # build multi-zone conservation problem with cuts portfolio
#' p2 <- problem(sim_pu_zones_stack, sim_features_zones) %>%
#'       add_min_set_objective() %>%
#'       add_relative_targets(matrix(runif(15, 0.1, 0.2), nrow = 5,
#'                                   ncol = 3)) %>%
#'       add_binary_decisions() %>%
#'       add_cuts_portfolio(10) %>%
#'       add_default_solver(gap = 0.2, verbose = FALSE)
#'
#' \dontrun{
#' # solve the problem
#' s2 <- solve(p2)
#'
#' # print solution
#' str(s2, max.level = 1)
#'
#' # plot solutions in portfolio
#' plot(stack(lapply(s2, category_layer)), main = "solution", axes = FALSE,
#'      box = FALSE)
#' }
#' @name add_cuts_portfolio
NULL

#' @rdname add_cuts_portfolio
#' @export
add_cuts_portfolio <- function(x, number_solutions = 10L) {
  # assert that arguments are valid
  assertthat::assert_that(inherits(x, "ConservationProblem"),
                          assertthat::is.count(number_solutions),
                          isTRUE(all(is.finite(number_solutions))))
  # add portfolio
  x$add_portfolio(pproto(
    "CutsPortfolio",
    Portfolio,
    name = "Cuts portfolio",
    parameters = parameters(
      integer_parameter("number_solutions", number_solutions,
                        lower_limit = 1L)),
    run = function(self, x, solver) {
      ## extract number of desired solutions
      n <- self$parameters$get("number_solutions")
      ## solve problem to verify that it is feasible
      sol <- solver$solve(x)
      ## if solving the problem failed then return NULL
      if (is.null(sol))
        return(sol)
      ## generate additional solutions
      sol <- list(sol)
      for (i in seq_len(n - 1) + 1) {
        ### add cuts
        rcpp_forbid_solution(x$ptr, sol[[i - 1]][[1]])
        ### solve solution
        curr_sol <- solver$solve(x)
        ### if contains valid solution then
        if (!is.null(curr_sol$x)) {
          sol[[i]] <- curr_sol
        } else {
          if ((i + 1) < self$parameters$get("number_solutions"))
            warning(paste("there are only", length(sol),
                          "feasible solutions within the optimality gap"))
          break()
        }
      }
    ## compile results
    return(sol)
  }
))}