deeplp 0.5.15

A package that solves Linear Programs using pytorch

deeplp

deeplp is a Python package for solving linear programming problems using deep learning techniques. It leverages PyTorch for its backend computations and provides a simple API for defining problems and training models.

Features

• Define linear programming problems with a simple API.
• Train deep learning models to solve LPs.
• Built-in support for plotting results and saving models.
• A command-line interface (CLI) for running experiments.
• Available on PyPI for easy installation.

Requirements

deeplp requires:

• Python 3.8+
• PyTorch (with GPU support if desired)

Installing PyTorch

Visit the PyTorch website for installation instructions. For example, to install PyTorch with CUDA 11.3 support on Windows:

pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118

For CPU-only support, run:

pip install torch torchvision torchaudio

Installation

You can install deeplp either from PyPI or directly from GitHub.

Installing from PyPI

pip install deeplp

Installing from GitHub

pip install git+https://github.com/yourusername/deeplp.git

If you're using Poetry for dependency management, add it to your pyproject.toml like so:

[tool.poetry.dependencies]
deeplp = "^0.1.0"  # or use the Git URL for the latest version

Basic Usage

Defining a Problem

Use the provided createProblem function to define your linear programming problem. For example:

from deeplp import createProblem

# Define your problem data:
c = [1.0, 2.0]                # Objective coefficients
A = [
    [3, -5],
    [3, -1],
    [3,  1],
    [3,  4],
    [1,  3]
]                           # Constraint matrix
b = [15, 21, 27, 45, 30]      # Right-hand side values
tspan = (0.0, 10.0)           # Time span

# Create the problem (if no name is provided, a name is generated automatically)
problem = createProblem(c, A, b, tspan, name=None, test_points=None)

Training a Model

The main training function has the following signature:

model, loss_list, mov = train(
    batches=2,
    batch_size=256,
    epochs=500,
    problems_ids=[1, 2],
    cases=[1, 2],
    do_plot=True,
    saving_dir="my_saved_models"
)

Running the CLI

After installing the package (which sets up a CLI entry point via Poetry), you can run:

deeplp --batches 1 --batch_size 128 --iterations 1000 --case 1 --example 1 --do_plot --folder saved_models

This command runs your CLI to start training with the specified options.