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Get raw solution vector from a solution

Source: R/get_solution.R
get_solution_vector.Rd

Return the raw decision-variable vector produced by the solver, in the internal model-variable order used by the optimization backend.

Usage

get_solution_vector(x, run = 1L)

Arguments

x

A Solution or SolutionSet object returned by solve.

run

Positive integer giving the run index to extract when x is a SolutionSet. Default is 1L.

Value

A numeric vector with one value per internal model variable.

Details

This function extracts the raw solution vector stored at x$solution$vector for a Solution or for a selected run of a SolutionSet.

The returned vector is in the internal variable order of the optimization model. Depending on the problem formulation, it may include:

  • planning-unit selection variables,

  • action-allocation variables,

  • auxiliary variables introduced for targets, budgets, fragmentation, or other constraints/objectives,

  • and potentially additional blocks created internally by the model builder.

Therefore, this vector is primarily intended for advanced users, debugging, diagnostics, or internal verification. It is not a user-facing allocation table.

To inspect selected planning units or selected actions in a more interpretable form, use get_pu or get_actions instead.

For a single solution, the returned vector corresponds to that solution. For a SolutionSet, the run argument selects which run to extract.

See also

get_pu, get_actions, get_features, get_targets

Examples

# \donttest{
if (requireNamespace("rcbc", quietly = TRUE)) {
  pu_tbl <- data.frame(
    id = 1:4,
    cost = c(1, 2, 3, 4)
  )

  feat_tbl <- data.frame(
    id = 1:2,
    name = c("feature_1", "feature_2")
  )

  dist_feat_tbl <- data.frame(
    pu = c(1, 1, 2, 3, 4),
    feature = c(1, 2, 2, 1, 2),
    amount = c(5, 2, 3, 4, 1)
  )

  actions_df <- data.frame(
    id = "conservation",
    name = "conservation"
  )

  effects_df <- data.frame(
    pu = c(1, 2, 3, 4),
    action = "conservation",
    feature = c(1, 1, 2, 2),
    benefit = c(2, 1, 1, 2),
    loss = c(0, 0, 0, 0)
  )

  p <- create_problem(
    pu = pu_tbl,
    features = feat_tbl,
    dist_features = dist_feat_tbl,
    cost = "cost"
  ) |>
    add_actions(actions_df, cost = 0) |>
    add_effects(effects_df) |>
    add_constraint_targets_relative(0.2) |>
    add_objective_min_cost() |>
    set_solver_cbc(time_limit = 10)

  sol <- solve(p)

  v <- get_solution_vector(sol)
  v
  length(v)
}
#> [1] 13
# }