A tidy data frame representation of assignment problems, suitable for use with grouped workflows and batch solving. Contains two independent 3x3 assignment problems in long format.
Format
A tibble with 18 rows and 4 columns:
- sim
Simulation/problem identifier. Integer with values 1 or 2, distinguishing two independent assignment problems. Use with
group_by(sim)for grouped solving.- source
Source node index. Integer 1-3 representing the row (source) in each 3x3 cost matrix.
- target
Target node index. Integer 1-3 representing the column (target) in each 3x3 cost matrix.
- cost
Cost of assigning source to target. Numeric values ranging from 1-7. Each source-target pair has exactly one cost entry.
Details
This dataset demonstrates couplr's data frame interface for LAP solving. The long format (one row per source-target pair) is converted internally to a cost matrix for solving.
Simulation 1: Costs from example_costs$simple_3x3
Optimal assignment: (1->2, 2->1, 3->3)
Total cost: 9
Simulation 2: Different cost structure
Optimal assignment: (1->1, 2->3, 3->3) or equivalent
Total cost: 4
Examples
library(dplyr)
# Solve both problems with grouped workflow
example_df |>
group_by(sim) |>
lap_solve(source, target, cost)
# Batch solving for efficiency
example_df |>
group_by(sim) |>
lap_solve_batch(source, target, cost)
# Inspect the data structure
example_df |>
group_by(sim) |>
summarise(
n_pairs = n(),
min_cost = min(cost),
max_cost = max(cost)
)