Solve multiple assignment problems efficiently — lap_solve

Solve many independent assignment problems at once. Supports lists of matrices, 3D arrays, or grouped data frames. Optional parallel execution via n_threads.

Usage

lap_solve_batch(
  x,
  source = NULL,
  target = NULL,
  cost = NULL,
  maximize = FALSE,
  method = "auto",
  n_threads = 1,
  forbidden = NA
)

Arguments

x: One of: List of cost matrices, 3D array, or grouped data frame
source: Column name for source indices (if x is a grouped data frame)
target: Column name for target indices (if x is a grouped data frame)
cost: Column name for costs (if x is a grouped data frame)
maximize: Logical; if TRUE, maximizes total cost (default: FALSE)
method: Algorithm to use (default: "auto"). See lap_solve for options.
n_threads: Number of threads for parallel execution (default: 1). Set to NULL to use all available cores.
forbidden: Value to mark forbidden assignments (default: NA)

Value

A tibble with columns:

problem_id: identifier for each problem
source: source indices for assignments
target: target indices for assignments
cost: cost of each assignment
total_cost: total cost for each problem
method_used: algorithm used for each problem

Examples

# List of matrices
costs <- list(
  matrix(c(1, 2, 3, 4), 2, 2),
  matrix(c(5, 6, 7, 8), 2, 2)
)
lap_solve_batch(costs)

# 3D array
arr <- array(runif(2 * 2 * 10), dim = c(2, 2, 10))
lap_solve_batch(arr)

# Grouped data frame
library(dplyr)
df <- tibble(
  sim = rep(1:5, each = 9),
  source = rep(1:3, times = 15),
  target = rep(1:3, each = 3, times = 5),
  cost = runif(45, 1, 10)
)
df |> group_by(sim) |> lap_solve_batch(source, target, cost)

# Parallel execution (requires n_threads > 1)
lap_solve_batch(costs, n_threads = 2)