CatOpt-Grid MVP

A production-friendly MVP for a category-theoretic, cross-domain distributed optimization framework. This repository implements the core primitives and a tiny ADMM-lite solver to help validate the architecture and provide a concrete starting point for adapters and cross-domain integration.

Key components

• LocalProblem: per-asset optimization task with a convex quadratic objective and bound constraints.
• SharedVariable: consensus variable used by all agents in the ADMM-like loop.
• ADMMLiteSolver: lightweight solver implementing x-update, z-update, and dual variable updates with bound projection.

Usage

• Install dependencies and run tests with the provided test.sh.
• This MVP focuses on correctness and stability for the ADMM-lite loop; cross-domain adapters and governance layers can be added in future iterations.

This repository is a stepping stone toward the CatOpt-Grid architecture described in AGENTS.md.

Architecture scaffolding: Bridge and Adapters

• catopt_grid.bridge: lightweight interoperability layer with IRObject/IRMorphism and a tiny GraphOfContracts registry to version adapters and data schemas.
• catopt_grid.adapters: starter adapters (rover_planner, habitat_module) that illustrate mapping local problems to the canonical IR and seed cross-domain interoperability.
• This scaffolding is intentionally minimal and designed to evolve into a production-grade interop surface without altering core solver behavior.

Roadmap

• Bridge and adapters: evolve to a production-ready interoperability surface with a robust Graph-of-Contracts, versioned schemas, and recoverable delta-sync semantics.
• Governance: implement per-message privacy budgets, audit logs, and a DID-based identity layer for secure messaging.
• Cross-domain MVP: extend with more adapters (energy, water, mobility, robotics) and a reference SDK with Python/C++ bindings; support codegen or bindings for edge devices (Rust/C).
• Global constraints: add a Limits/Colimits layer to enforce fleet policies without re-deriving global models; deterministic reconciliation on reconnects.
• Evaluation: formal convergence guarantees for broader convex/weakly convex classes; HIL validation and privacy budget accounting.