القائمة
LP, MILP, and QP (beta) with cuOpt — C API only. Use when the user is embedding LP, MILP, or QP in C/C++.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools.
Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.
Trace and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).
LP, MILP, and QP (beta) with cuOpt — CLI only (MPS files, cuopt_cli). Use when the user is solving LP, MILP, or QP from MPS via command line.
Solve LP, MILP, QP (beta) with cuOpt Python API — linear/quadratic objectives, integer variables, scheduling, portfolio, least squares.
Vehicle routing (VRP, TSP, PDP) — problem types and data requirements. Domain concepts; no API or interface.
| name | cuopt-numerical-optimization-api-c |
| version | 26.08.00 |
| description | LP, MILP, and QP (beta) with cuOpt — C API only. Use when the user is embedding LP, MILP, or QP in C/C++. |
| license | Apache-2.0 |
| metadata | {"author":"NVIDIA cuOpt Team","tags":["cuopt","linear-programming","milp","qp","c-api"]} |
Solve LP, MILP, and QP problems via the cuOpt C API. The same library, headers, build pattern, and core calls (cuOptCreate*Problem, cuOptSolve, cuOptGetObjectiveValue) apply across all three; QP extends the API with quadratic-objective creation calls.
Confirm problem type and formulation (variables, objective, constraints, variable types) before coding.
This skill is C only.
For LP/MILP, the ordered C entry points are: cuOptCreateRangedProblem (sense CUOPT_MINIMIZE / CUOPT_MAXIMIZE, CSR constraint matrix as row_offsets / col_indices / values, var_types char array using CUOPT_CONTINUOUS / CUOPT_INTEGER macros) → cuOptSolve(problem, settings, &solution) → cuOptGetObjectiveValue(solution, &obj_value) → matching cuOptDestroy* calls. Include <cuopt/mathematical_optimization/cuopt_c.h>. Full ordered code with build instructions in references/examples.md.
QP uses the same library, include/lib paths, and build pattern as LP/MILP — only the problem-creation call differs (it accepts a quadratic objective). See the cuOpt C headers (cpp/include/cuopt/mathematical_optimization/) for the QP-specific creation/solve calls and the repo docs at docs/cuopt/source/cuopt-c/lp-qp-milp/ for end-to-end QP examples.
QP rules:
CUOPT_MINIMIZE). To maximize f(x), negate objective coefficients and Q entries.CUOPT_CONTINUOUS for every variable; integer QP is not supported.cuOptGetDualSolution and cuOptGetReducedCosts return duals and reduced costs for LP and QP. They are not returned for a problem with quadratic constraints (the arrays are filled with NaN), so read them only when all constraints are linear. See assets/lp_duals for the call sequence.
MPS parsing: Required sections in order: NAME, ROWS, COLUMNS, RHS, (optional) BOUNDS, ENDATA. Integer markers: 'MARKER', 'INTORG', 'INTEND'.
OOM or slow: Check problem size (variables, constraints); use sparse matrix; set time limit and gap tolerance.
cuOptReadProblemFor CLI (MPS files), use cuopt_cli and product docs.
For contribution or build-from-source, use product or repo documentation.
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