Provides a methodology for formulating, comparing, validating, and auditing constrained optimization decisions using explicit variables, objectives, feasibility rules, tradeoffs, baselines, and solver evidence.
Define, compare, and audit constrained optimization decisions with explicit variables, units, hard and soft constraints, feasibility, Pareto tradeoffs, and honest solver evidence. Do not use for statistical modeling or ML fitting, which belong to data-scientist or ml-engineering, or for operating a named solver.
evals/evals.json
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README.md
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templates/formulation-record.md
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templates/solution-audit.md
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Reference
references/formulation-and-evidence.md
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references/multiobjective-and-robustness.md
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Reference
references/source-index.md
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SKILL.md
SKILL.md
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