lap_solve_batch: Solve multiple assignment problems efficiently

In couplr: Optimal Pairing and Matching via Linear Assignment

Solve multiple assignment problems efficiently

Description

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)

couplr documentation built on Jan. 20, 2026, 5:07 p.m.