# CatOpt: Category-Theoretic Compositional Optimization (MVP)

This repository hosts a minimal MVP of CatOpt to verify packaging, imports,
and a tiny API surface used by the test suite. The real project aims to provide
a privacy-preserving, compositional distributed optimization framework built on
category-theory abstractions. This readme documents the current MVP scope and
how to extend it.

- MVP surface: a simple add(a, b) function to validate packaging and imports.
- Packaging: aligned with pyproject.toml, using setuptools to build a wheel.
- Extensibility: planned steps include a DSL for local problems, data contracts,
  an ADMM-like solver kernel, and adapters for edge devices.

Usage
- Run tests and build: ./test.sh
- Import in Python:
  from catopt_category_theoretic_compositional import add
  assert add(2, 3) == 5