21 lines
1.4 KiB
Markdown
21 lines
1.4 KiB
Markdown
# catopt-category-theoretic-compositional-
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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 compositional optimization framework that can be plugged into existing energy and robotics ecosystems with privacy by design and offline resilience.
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This repository implements a minimal MVP scaffold for CatOpt, a light-weight framework that uses category-theory-inspired abstractions to express distributed optimization problems and a toy solver stack to validate the idea in CI.
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MVP Runtime (new): lightweight primitives to experiment with CatOpt concepts locally.
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- LocalProblem: representation of an agent's optimization task.
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- DataContract, SharedVariables, PlanDelta: lightweight data-exchange primitives for a privacy-conscious mesh.
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- ADMMTwoAgentSolver: tiny, in-process ADMM-like solver to demonstrate joint optimization across two agents.
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- demo_two_agent_admm(): quick in-repo demonstration function returning final variables.
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Usage notes:
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- Import from the package and optionally run the demo function to observe convergence on a toy problem.
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- Example:
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```python
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from catopt_category_theoretic_compositional.runtime import ADMMTwoAgentSolver, demo_two_agent_admm
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result = demo_two_agent_admm()
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print(result)
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```
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