Results for “milp”

LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.

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LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.

The English summary is being prepared. Showing the original description.

LP, MILP, QP — concepts, problem-text parsing, and formulation patterns (parameters, constraints, decisions, objective). Concepts only; no API.

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LP, MILP, QP — concepts, problem-text parsing, and formulation patterns (parameters, constraints, decisions, objective). Concepts only; no API.

The English summary is being prepared. Showing the original description.

Formulates LP, MILP, CP-SAT models; diagnoses infeasibility and gaps. Use when a MILP is infeasible, shifts need scheduling under capacity, or choosing a solver.

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シミュレーションと数理最適化（モンテカルロ / Monte Carlo / 離散事象シミュレーション / DES / 待ち行列 / リスク分析 / シナリオ分析、線形計画 / LP / 整数計画 / MIP / スケジューリング / 配送計画 / VRP / 割当 / メタヒューリスティクス）を 実行したら必ずセットで出す図と値のルーター。SeedSequence.spawn, scipy.stats の rvs, simpy, SALib, pulp, LpProblem, mip.Model, ortools, pywraplp, cp_model, pyomo, cvxpy, linprog, milp がコードに現れたとき、または ユーザーが「シミュレーションして」「モンテカルロで見積もって」「待ち時間をシミュレートして」「最適化して」 「最適な割当を求めて」「スケジュールを組んで」と言ったときに使う。収束・標準誤差・ソルバー status・制約違反・ 感度分析に言及がなくても適用する。SKILL.md のルーティング表で手法を特定し、対応する references/<手法>.md を 読んでから実行する。MCMC は statistical-inference-diagnostics、ML のハイパラ探索は predictive-modeling-diagnostics を使う。

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Guide the use of NVIDIA cuOpt for route planning and mathematical optimization, choosing values to minimize cost or maximize profit under constraints. Covers requirements, environment checks, installation guidance, and verification of solver results.

• Clarify vehicle routing requirements and constraints
• Plan how to use cuOpt through Python, REST, C, or CLI
• Review installation and server setup options

Use for formulating, solving, debugging, and validating mixed-integer linear optimization models with open-source solvers, including variable indexing, sparse constraints, linearized costs, solver limits, MIP gaps, incumbent extraction, numerical tolerances, and deterministic output reporting.

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Base rules for end users calling NVIDIA cuOpt (routing/LP/MILP/QP/install/server). Not for cuOpt internals — use cuopt-developer for those.

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