32 lines
1.6 KiB
Markdown
32 lines
1.6 KiB
Markdown
Overview
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- This repository contains a minimal MVP implementation scaffold for CatOpt (Category-Theoretic Compositional Optimization).
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- It includes a tiny Python package as a placeholder and a basic test to verify the test harness works in CI.
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Architecture
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- Language: Python (src/catopt_category_theoretic_compositional_)
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- Core ideas are stubbed to a simple additive function to demonstrate packaging, imports, and tests.
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Testing and CI
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- Tests are written with pytest (tests/test_basic.py).
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- test.sh runs a wheel build via python -m build and executes pytest to validate the package compiles and tests pass.
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- test.sh must be executable in CI and root-level.
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Commands
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- Local test: ./test.sh
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- Build metadata: pyproject.toml will be used by the build tooling.
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Guidance for contributors
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- Do not change the MVP skeleton without explicit intent to expand functionality.
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- Ensure test.sh remains executable and tests cover basic import/build scenarios.
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- Update AGENTS.md as the architecture evolves.
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New MVP scaffolds (current status):
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- Added lightweight DSL primitives (src/catopt_category_theoretic_compositional/dsl.py)
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- Added minimal contracts primitives (src/catopt_category_theoretic_compositional/contracts.py)
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- Added a tiny solver kernel (src/catopt_category_theoretic_compositional/solver.py)
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- Exposed new API in package __init__ (LocalProblem, SharedVariables, PlanDelta, admm_update)
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How to explore:
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- Import surface: from catopt_category_theoretic_compositional import LocalProblem, SharedVariables, PlanDelta, admm_update
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- Use admm_update(local, shared) as a tiny, deterministic update example.
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