compile_model: Compile the optimization model stored in a Problem

In multiscape: Multi-Action, Multi-Objective Spatial Planning

Compile the optimization model stored in a Problem

Description

Materializes the optimization model represented by a Problem object without solving it. This is an advanced function mainly intended for debugging, inspection, and explicit model preparation.

In standard workflows, users normally do not need to call this function, because solve compiles the model automatically when needed.

Usage

compile_model(x, force = FALSE, ...)

## S3 method for class 'Problem'
compile_model(x, force = FALSE, ...)

Arguments

x	A Problem object.
force	Logical. If TRUE, rebuild the model even if a current compiled model already exists.
...	Reserved for future extensions.

Details

Use this function when you want to prepare the optimization model explicitly before solving, inspect compiled model structures, or verify that the problem compiles successfully.

Conceptually, a Problem object stores a declarative optimization specification: planning data, actions, effects, targets, constraints, objectives, spatial relations, and optional method or solver settings. compile_model() transforms that stored specification into an internal compiled model representation that can later be reused by the solving layer.

The exact compiled representation is implementation-specific, but it may include indexed variables, prepared constraint blocks, objective structures, and internal model snapshots or pointers.

Compilation does not solve the optimization problem. Therefore, a problem may compile successfully and still later be infeasible, numerically difficult, or otherwise fail during solver execution.

Value

A Problem object with compiled model structures stored internally.

See Also

solve

Examples

# Load a complete simulated planning problem.
example_data <- load_sim_multiaction()

x <- create_problem(
  pu = example_data$planning_units,
  features = example_data$features,
  dist_features = example_data$dist_features,
  cost = "cost"
) |>
  add_actions(
    example_data$actions,
    cost = example_data$action_costs
  ) |>
  add_effects(
    example_data$effect_assumptions
  ) |>
  add_constraint_targets_relative(0.3) |>
  add_objective_min_cost(alias = "cost")

x <- compile_model(x)

# Force recompilation
x <- compile_model(x, force = TRUE)

multiscape documentation built on Oct. 12, 2026, 5:08 p.m.