py-lap-solver 0.1.1

Unified Python framework for Linear Assignment Problem solvers

py-lap-solver

A unified Python framework for Linear Assignment Problem (LAP) solvers.

Overview

py-lap-solver provides a common interface for multiple LAP solver implementations, ranging from pure Python (scipy) to optimized C++ implementations with OpenMP and CUDA support.

The Linear Assignment Problem seeks to find an optimal assignment between two sets given a cost matrix, minimizing (or maximizing) the total cost of the assignment.

Installation

Install from pypi

pip install py-lap-solver

Or install from source

git clone [e-mail]:nikitapond/py-lap-solver.git

# Install the package in editable mode
pip install -e .

# Install with development dependencies
pip install -e ".[dev]"

Features

• Unified Interface: Common API across all solver implementations
• Multiple Backends:
  • ScipySolver: Pure Python implementation using scipy's Hungarian algorithm
  • BatchedScipySolver: C++ implementation with OpenMP parallelization for batch processing
  • Lap1015Solver: Highly optimized C++ implementation (shortest augmenting path algorithm)
• Batch Processing: Solve multiple LAP instances efficiently with OpenMP parallelization
• Flexible Input: Support for square and rectangular cost matrices
• Optional GPU Support: CUDA support in LAP1015 (not yet fully exposed in Python bindings)

Quick Start

Using the Solver Registry (Recommended)

The easiest way to use the solvers is through the pre-configured Solvers registry:

from py_lap_solver.solvers import Solvers
import numpy as np

# Create a batch of cost matrices
batch_matrices = np.random.rand(100, 500, 500)

# Use the fastest available solver with OpenMP parallelization
# This will give you ~6x speedup over sequential processing
assignments = Solvers.BatchedScipyOMP.batch_solve(batch_matrices)

# For single problems, use the standard scipy solver
cost_matrix = np.random.rand(500, 500)
single_assignment = Solvers.Scipy.solve_single(cost_matrix)

Available solvers in the registry:

• Solvers.Scipy - Pure Python scipy implementation (always available)
• Solvers.BatchedScipyOMP - C++ scipy with OpenMP batch parallelization
• Solvers.BatchedScipySequential - C++ scipy without parallelization
• Solvers.Lap1015OMP - LAP1015 algorithm with OpenMP (limited benefit)
• Solvers.Lap1015Sequential - LAP1015 algorithm without OpenMP

Manual Configuration

You can also instantiate solvers directly with custom parameters:

from py_lap_solver.solvers import ScipySolver, BatchedScipySolver, Lap1015Solver
import numpy as np

# Use scipy solver (always available)
scipy_solver = ScipySolver()
assignments = scipy_solver.solve_single(cost_matrix)

# Use batched scipy solver with runtime OpenMP control
if BatchedScipySolver.is_available():
    # Create solver with OpenMP enabled (default)
    batch_solver_omp = BatchedScipySolver(use_openmp=True)

    # Create solver without OpenMP for comparison
    batch_solver_seq = BatchedScipySolver(use_openmp=False)

    batch_matrices = np.random.rand(10, 100, 100)
    fast_assignments = batch_solver_omp.batch_solve(batch_matrices)  # ~6x faster
    slow_assignments = batch_solver_seq.batch_solve(batch_matrices)

# Use LAP1015 solver
if Lap1015Solver.is_available():
    # Note: OpenMP provides minimal benefit for LAP1015 due to algorithm structure
    lap_solver = Lap1015Solver(use_openmp=False)
    assignments = lap_solver.solve_single(cost_matrix)

Return Format

All solvers return assignments in a consistent format:

• Single problem: 1D array of shape (N,) where result[i] is the column assigned to row i
• Batch problem: 2D array of shape (B, N) where result[b, i] is the column assigned to row i in batch b
• Unassigned rows are marked with -1 (or custom unassigned_value)

import numpy as np
from py_lap_solver.solvers import Solvers

cost_matrix = np.array([[1, 2], [3, 4]])
assignments = Solvers.Scipy.solve_single(cost_matrix)
# assignments = [1, 0]  (row 0 -> col 1, row 1 -> col 0)

Building with C++ Extensions

To enable the optimized C++ solvers, you need CMake and build tools:

# Install build dependencies
pip install scikit-build-core pybind11

# Build and install with C++ extensions
pip install -e . --no-build-isolation

# On macOS, you may need to install libomp for OpenMP support
brew install libomp

OpenMP Runtime Control

All C++ solvers support runtime OpenMP control through the use_openmp parameter:

from py_lap_solver.solvers import BatchedScipySolver
import numpy as np

# Create solver with OpenMP enabled
solver_parallel = BatchedScipySolver(use_openmp=True)

# Create solver without OpenMP
solver_sequential = BatchedScipySolver(use_openmp=False)

batch = np.random.rand(100, 500, 500)

# Parallel: ~126ms for 100 matrices
assignments_fast = solver_parallel.batch_solve(batch)

# Sequential: ~762ms for 100 matrices
assignments_slow = solver_sequential.batch_solve(batch)

Why OpenMP Helps for Batched Scipy but Not LAP1015:

• BatchedScipySolver: Each matrix in the batch is independent → perfect parallelization with #pragma omp parallel for
• LAP1015Solver: Complex intra-matrix data dependencies → synchronization barriers dominate, killing performance

Recommended Usage Patterns

from py_lap_solver.solvers import Solvers
import numpy as np

# Pattern 1: Batch processing (FAST - use OpenMP)
batch_matrices = np.random.rand(1000, 100, 100)
assignments = Solvers.BatchedScipyOMP.batch_solve(batch_matrices)

# Pattern 2: Single large problem (no parallelization benefit)
single_matrix = np.random.rand(5000, 5000)
assignment = Solvers.Scipy.solve_single(single_matrix)  # or BatchedScipySequential

# Pattern 3: Many small problems in a loop
for i in range(1000):
    matrix = generate_matrix()
    # BAD: Calling solve_single in a loop
    result = Solvers.Scipy.solve_single(matrix)

# Better: Batch them together
all_matrices = np.array([generate_matrix() for _ in range(1000)])
results = Solvers.BatchedScipyOMP.batch_solve(all_matrices)  # 6x faster!

Development

Installation

# Install with development dependencies (includes black, ruff, pytest)
pip install -e ".[dev]"

Code Formatting and Linting

The project uses black for code formatting and ruff for linting. A Makefile is provided for convenience:

# Format code with black
make format

# Lint code with ruff
make lint

# Auto-fix linting issues
make lint-fix

# Run all checks
make check

# Format, lint-fix, check, and test in one command
make all

Or use the tools directly:

# Format code
black src/ tests/

# Lint code
ruff check src/ tests/

# Auto-fix linting issues
ruff check --fix src/ tests/

Testing

# Run tests with pytest
pytest tests/

# Or use make
make test

License

MIT