couplr (version 1.0.10)

lap_solve: Solve linear assignment problems

Description

Provides a tidy interface for solving the linear assignment problem using 'Hungarian' or 'Jonker-Volgenant' algorithms. Supports rectangular matrices, NA/Inf masking, and data frame inputs.

Usage

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

Value

A tibble with columns:

• source: row/source indices

• target: column/target indices

• cost: cost of each assignment

• total_cost: total cost (attribute)

Arguments

x

Cost matrix, data frame, or tibble. If a data frame/tibble, must include columns specified by source, target, and cost.

source

Column name for source/row indices (if x is a data frame)

target

Column name for target/column indices (if x is a data frame)

cost

Column name for costs (if x is a data frame)

maximize

Logical; if TRUE, maximizes total cost instead of minimizing (default: FALSE)

method

Algorithm to use. One of:

• "auto" (default): automatically selects best algorithm

• "jv": 'Jonker-Volgenant' algorithm (general purpose, fast)

• "hungarian": Classic 'Hungarian' algorithm

• "auction": 'Bertsekas' auction algorithm (good for large dense problems)

• "sap": Sparse assignment (good for sparse/rectangular problems)

• "hk01": 'Hopcroft-Karp' for binary/uniform costs

forbidden

Value to mark forbidden assignments (default: NA). Can also use Inf.

Examples

# Matrix input
cost <- matrix(c(4, 2, 5, 3, 3, 6, 7, 5, 4), nrow = 3)
lap_solve(cost)

# Data frame input
library(dplyr)
df <- tibble(
  source = rep(1:3, each = 3),
  target = rep(1:3, times = 3),
  cost = c(4, 2, 5, 3, 3, 6, 7, 5, 4)
)
lap_solve(df, source, target, cost)

# With NA masking (forbidden assignments)
cost[1, 3] <- NA
lap_solve(cost)

# Grouped data frames
df <- tibble(
  sim = rep(1:2, each = 9),
  source = rep(1:3, times = 6),
  target = rep(1:3, each = 3, times = 2),
  cost = runif(18, 1, 10)
)
df |> group_by(sim) |> lap_solve(source, target, cost)