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Problem space: distributed, offline-first optimization across heterogeneous edge devices (DERs, meters, controllers, EV chargers) in mesh networks. Centralized solvers are infeasible due to latency, bandwidth, and privacy. We need a modular, provably
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README.md

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