Map interpreted optimization problems into cuOpt-native models for the fast path with minimal clarifying questions.
Idioma del texto original: inglés
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Map interpreted optimization problems into cuOpt-native models for the fast path with minimal clarifying questions.
Idioma del texto original: inglés
Run cuOpt in the NemoClaw sandbox — probe/smoke gates, prefer cancelable Python gRPC jobs, use legacy remote execution only when that API is unavailable, then vendored cuOpt skills.
Idioma del texto original: inglés
Choose fast direct-to-cuOpt solve versus replayable or auditable model artifact mode.
Idioma del texto original: inglés
Infer optimization structure from uploaded tables and identify minimal clarifications before cuOpt modeling.
Idioma del texto original: inglés
Troubleshoot cuOpt LP/MILP problems including errors, wrong results, infeasible solutions, performance issues, and status codes. Use when the user says something isn't working, gets unexpected results, or needs help diagnosing issues.
Idioma del texto original: inglés
Coordinate uploaded data plus a natural-language question into interpretation, clarification, cuOpt solve, and a user-facing answer.
Idioma del texto original: inglés
Classify whether a data-backed request is LP, MILP, QP, routing, or non-optimization analytics.
Idioma del texto original: inglés
Multi-period supply chain planning model: data files, BOM structure, variable/constraint reference for the max-supply base model.
Idioma del texto original: inglés
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