A discrete optimization package with interior point and exterior swagger
Command
Read discopt benchmark result JSON and produce a narrative performance report — solve counts, timing (shifted geometric mean), solution quality, layer profiling, performance profiles, and regression detection. Use to analyze or compare benchmark runs.
Command
Translate a discopt model to or from another algebraic modeling language (Pyomo, GAMS, AMPL, JuMP) or file format. Use to port a model in/out of discopt. For machine file formats (.gms/.nl/.mps/.lp), prefer the built-in discopt convert CLI.
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Debug a broken discopt setup, model, or solve. Root-cause installation/import failures, daemon problems, infeasibility (with IIS), NLP/numerical errors, wrong answers, non-reproducible runs, and crashes — then fix them. Use when something is broken or behaving unexpectedly, as opposed to /diagnose which interprets a…
Command
Interpret a discopt SolveResult or solver output and recommend concrete next steps — status, gap, timing, and layer profiling. Use for a solve that completed (optimal/feasible/timelimit/etc.) and you want to understand or improve it. For something broken, crashing, or infeasible, use /debug instead.
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Fit unknown model parameters to experimental data with discopt — build an Experiment, run estimateparameters(), and interpret estimates, confidence intervals, FIM, and identifiability. Use for parameter estimation / regression / model calibration from data.
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Read a discopt model and produce a formal mathematical formulation (LaTeX/Markdown) — sets, parameters, variables, objective, constraints — plus plain-English descriptions. Use to document a model or generate its math write-up.
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Translate a natural-language optimization problem into a complete, runnable discopt model — variables, objective, constraints, validation, and a solve call. Use when the user describes a problem in words and wants discopt model code.
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Analyze a discopt model and suggest reformulations that strengthen relaxations, tighten bounds, exploit structure, or speed up the solve — big-M, bilinear/McCormick, RLT, PSD/SOC, GDP hull, geometric programming, decomposition, FBBT. Use to make an existing model solve faster or close the gap.