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cuopt-numerical-optimization-api
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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Guides Holoscan SDK installation: inspects the host, assesses platform compatibility, recommends an install method, and delegates to the matching install skill.
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).
Use when running people attribute search (PAS) image augmentation and auto-labeling workflows on OSMO: flow selection, preflight, submit-time interpolation, monitoring, and output retrieval. Trigger keywords: people attribute search, PAS, person augmentation, attribute search, person re-identification, clothing augmentation, person crop augmentation.
Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
Calibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API. Use when user has local MP4s and says 'calibrate my videos', 'run AMC on these videos', or similar.
| name | cuopt-numerical-optimization-api |
| version | 26.08.00 |
| description | 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. |
| license | Apache-2.0 |
| metadata | {"author":"NVIDIA cuOpt Team","tags":["cuopt","linear-programming","milp","qp","python","c-api","cli"]} |
Model and solve LP, MILP, and QP problems using NVIDIA cuOpt's GPU-accelerated solver.
Choose the reference for the user's interface:
| Interface | When to use | Reference |
|---|---|---|
| Python | User is writing Python code | references/python_api.md |
| C / C++ | User is embedding in a C/C++ application | references/c_api.md |
| CLI | User is solving from MPS files on the command line | references/cli_api.md |
If the interface is not yet clear, ask before writing any code.
Already using a modeling language? cuOpt also works as a solver backend for third-party modeling tools — AMPL, GAMS / GAMSPy, PuLP, JuMP, Pyomo, and CVXPY — with near-zero code changes (point the model's solver at cuOpt). CVXPY additionally covers convex QP and, in beta, QCQP / SOCP. Prefer this when the user already has a model in one of these tools rather than porting it to the cuOpt API. See Third-Party Modeling Languages.
Decide from the objective and variables:
| If the objective is... | And variables are... | Use |
|---|---|---|
Linear (sum of c_i * x_i) | All continuous | LP |
| Linear | Some integer or binary | MILP |
Has squared (x*x) or cross (x*y) terms | Continuous (integer QP not supported) | QP (beta) |
Prefer LP when the problem allows it. LP solves faster and has stronger optimality guarantees. Use MILP only when the problem logically requires whole numbers or yes/no decisions. Use QP only when the objective is genuinely quadratic (variance, squared error, kinetic energy).
x*x or x*y terms (portfolio optimization, least squares, regularized regression).| Problem wording / concept | Variable type | Examples |
|---|---|---|
| Discrete entities (counts) | INTEGER | Workers, cars, trucks, machines, pilots, facilities, units to manufacture |
| Yes/no or on/off | INTEGER (binary, lb=0 ub=1) | Open a facility, run a machine, assign a person to a shift |
| Amounts that can be fractional | CONTINUOUS | Tonnes, litres, dollars, hours, kWh, proportion of capacity |
| Rates or fractions | CONTINUOUS | Utilization, percentage, share of budget |
Rule of thumb: "How many things" → INTEGER. "How much" → CONTINUOUS.
f(x), minimize -f(x) and negate the reported objective value.Duals and reduced costs are available for LP and QP only:
NaN.| Problem | Likely cause | Fix |
|---|---|---|
| Infeasible | Conflicting constraints | Check constraint logic and bounds |
| Unbounded | Missing bounds | Add variable bounds |
| Slow solve | Large problem | Set time limit; increase gap tolerance |
| QP rejected with MAXIMIZE | QP only supports MINIMIZE | Negate the objective; negate the result |
| QP returns non-optimal | Q not PSD or badly scaled | Check Q is PSD; rescale variables |
| Setting | Purpose |
|---|---|
time_limit | Stop after N seconds |
mip_relative_gap | Stop MILP when within X% of optimal |
mip_absolute_tolerance | Absolute MIP gap stop |
log_to_console | Enable solver logging |
Syntax varies by interface — see the interface reference file.