26 lines
1.5 KiB
Markdown
26 lines
1.5 KiB
Markdown
# CatOpt: Category-Theoretic Compositional Optimizer (MVP)
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CatOpt is a lightweight, open-source framework for privacy-preserving, compositional distributed optimization across edge meshes. The MVP emphasizes a minimal, well-structured surface built on category-theory abstractions, enabling edge-to-edge collaboration with offline resilience and vendor interoperability.
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What you get in this MVP
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- Local problems expressed by agents (objects)
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- Data exchange channels (morphisms) and problem transformers (functors)
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- Global assembly via Limits/Colimits and a lightweight ADMM-like solver
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- Privacy-by-design data contracts and modular adapters
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- A tiny Python runtime surface suitable for rapid prototyping and CI validation
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Why this matters
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- Composability: add/remove agents without re-deriving the global problem
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- Privacy: only exchanged, abstracted quantities per contracts
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- Convergence: disciplined, modular solver structure with verifiable properties
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- Interoperability: bridges to existing energy/robotics ecosystems via a common DSL
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Usage (quick start)
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- The core runtime primitives live in src/catopt_category_theoretic_compositional_/
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- The tiny DSL bridge to contracts lives in src/catopt_category_theoretic_compositional_/dsl.py
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- Importing and testing is kept simple to satisfy the CI gate (see tests/test_basic.py)
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For more details, see tests, the MVP runtime, and the protocol DSL (ProtocolContract, build_minimal_contract).
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This README is a starting point. As we evolve, we will add more examples, adapters, and end-to-end demos.
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