Cuopt Numerical Optimization API
NVIDIAOfficialCreated by NVIDIA cuOpt Team
- Skill
- Tool integration
- Software development
- Apache-2.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The cuOpt Numerical Optimization API provides interfaces to model and solve Linear Programming (LP), Mixed-Integer Linear Programming (MILP), and Quadratic Programming (QP) problems. It supports integration through Python, C, and a command-line interface. The tool is designed to work as a standalone solver or as a backend for third-party modeling languages such as AMPL, GAMS, PuLP, JuMP, Pyomo, and CVXPY. It includes features for setting solver parameters like time limits and tolerances, and provides access to primal solutions, objective values, and dual solutions for supported problem types.
What it does
- Solves LP, MILP, and QP problems using a GPU-accelerated solver.
- Provides C and Python APIs for programmatic model definition and solving.
- Offers a command-line interface for solving problems from MPS, QPS, and LP files.
- Supports integration with third-party modeling tools like Pyomo, PuLP, and JuMP.
- Allows configuration of solver settings including time limits and gap tolerances.
- Retrieves primal solutions, objective values, and dual values for LP and QP models.
When to use it
- When you need to solve linear or quadratic optimization problems.
- When you are working with discrete entities or yes/no decisions requiring MILP.
- When you have an existing model in a third-party tool and need a solver backend.
- When you need to solve optimization problems from standard MPS or LP files via CLI.
Advantages
- Supports multiple interfaces including Python, C, and CLI.
- Compatible with common third-party modeling languages.
- Provides granular control over solver parameters and settings.
- Allows for programmatic export and import of standard optimization file formats.
Usage examples
- “How do I solve a linear programming problem using the C API?”
- The agent provides a code example using cuOptCreateRangedProblem and cuOptSolve, demonstrating how to define constraints and variables in C.
- “Can I use cuOpt with Pyomo?”
- The agent explains that cuOpt works as a solver backend for Pyomo and provides guidance on pointing the model's solver to cuOpt.
- “How do I run a solver on an MPS file?”
- The agent instructs the user to use the cuopt_cli command followed by the file path and any necessary flags for solver settings.
- “What is the difference between LP and MILP in cuOpt?”
- The agent clarifies that LP is for continuous variables while MILP is for discrete or binary variables, and explains the implications for dual values.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/cuopt-numerical-optimization-api/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip cuopt-numerical-optimization-api.zip -d .agents/skills
Documentation by NVIDIA cuOpt Team
cuOpt Numerical Optimization API
Model and solve LP, MILP, and QP problems using NVIDIA cuOpt's GPU-accelerated solver.
Interface Selection
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.
Choosing LP vs MILP vs QP
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).
Use LP when every quantity can meaningfully be fractional: flows, proportions, rates, dollars, hours, tonnes of material, etc.
Use MILP when the problem mentions counts of discrete entities, yes/no choices, or either/or decisions (e.g. open a facility or not, assign a person to a shift, number of trucks).
Use QP when the objective minimizes variance, squared error, or any expression with
x*xorx*yterms (portfolio optimization, least squares, regularized regression).
Integer vs Continuous from Wording
| 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.
QP Rules (all interfaces)
MINIMIZE only — the solver rejects MAXIMIZE for quadratic objectives. To maximize
f(x), minimize-f(x)and negate the reported objective value.Continuous variables only — integer QP is not supported.
Q should be positive semi-definite for a convex, well-posed problem.
Beta — API may evolve; treat as production-capable for typical convex QP.
Dual Values
Duals and reduced costs are available for LP and QP only:
MILP — no duals (integer optima are not continuous).
Quadratic constraints — duals unavailable even for LP/QP; all values return
NaN.PDLP warmstart — LP only; MILP solves do not accept a PDLP warmstart.
Common Issues (all interfaces)
| 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 |
Solver Settings (concepts)
| 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.
Files
- ATTRIBUTION.mdAdded by AutoGineer965 B
- BENCHMARK.md4.8 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md4.8 KiB
- assets/c/README.md1.7 KiB
- assets/c/lp_basic/README.md466 B
- assets/c/lp_basic/lp_simple.c3.6 KiB
- assets/c/lp_duals/README.md500 B
- assets/c/lp_duals/lp_duals.c4.0 KiB
- assets/c/lp_warmstart/README.md289 B
- assets/c/milp_basic/README.md405 B
- assets/c/milp_basic/milp_simple.c3.4 KiB
- assets/c/milp_production_planning/README.md459 B
- assets/c/milp_production_planning/milp_production.c3.5 KiB
- assets/c/mps_solver/README.md590 B
- assets/c/mps_solver/data/sample.mps490 B
- assets/c/mps_solver/mps_solver.c3.4 KiB
- assets/cli/README.md856 B
- assets/cli/lp_production/README.md192 B
- assets/cli/lp_production/production.mps375 B
- assets/cli/lp_simple/README.md191 B
- assets/cli/lp_simple/sample.mps490 B
- assets/cli/milp_facility/README.md199 B
- assets/cli/milp_facility/facility.mps694 B
- assets/python/README.md581 B
- assets/python/least_squares/README.md136 B
- assets/python/least_squares/model.pyScript831 B
- assets/python/lp_basic/README.md207 B
- assets/python/lp_basic/model.pyScript1.1 KiB
- assets/python/lp_duals/README.md227 B
- assets/python/lp_duals/model.pyScript1.2 KiB
- assets/python/lp_warmstart/README.md140 B
- assets/python/lp_warmstart/model.pyScript1.8 KiB
- assets/python/maximization_workaround/README.md152 B
- assets/python/maximization_workaround/model.pyScript736 B
- assets/python/milp_basic/README.md433 B
- assets/python/milp_basic/incumbent_callback.pyScript1.8 KiB
- assets/python/milp_basic/model.pyScript1.2 KiB
- assets/python/milp_production_planning/README.md165 B
- assets/python/milp_production_planning/model.pyScript1.2 KiB
- assets/python/mps_solver/README.md2.2 KiB
- assets/python/mps_solver/data/README.md2.0 KiB
- assets/python/mps_solver/data/sample.mps490 B
- assets/python/mps_solver/model.pyScript8.2 KiB
- assets/python/mps_solver/results.md2.5 KiB
- assets/python/portfolio/README.md232 B
- assets/python/portfolio/model.pyScript1.6 KiB
- evals/evals.json13.7 KiB
- references/c_api.md4.7 KiB
- references/cli_api.md5.5 KiB
- references/python_api.md4.7 KiB
- references/qp_examples.md5.7 KiB
- skill-card.md4.3 KiB
- skill.oms.sig15.9 KiB
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- NVIDIA cuOpt Team
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/cuopt-numerical-optimization-api · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by NVIDIA cuOpt Team or NVIDIA.
Related skills
Implement
Matt Pocock· Community
A structured workflow for implementing software tasks from specifications or tickets.
Software developmentClaude CodeClaude appsOpenAI Codex+14 moreMITGitHub stars of the source repository317.5k
Source Driven Development
Addy Osmani· Community
A rigorous process for grounding code implementation in official, version-specific documentation to ensure accuracy and avoid outdated patterns.
Software developmentClaude CodeClaude appsOpenAI Codex+14 moreMITGitHub stars of the source repository113.9k
Write Swift
Emil Kowalski· Community
A comprehensive guide to writing modern, safe, and performant Swift code following the language's latest idioms and concurrency models.
Software developmentClaude CodeClaude appsOpenAI Codex+14 moreMITGitHub stars of the source repository54.3k
Cardputer Buddy
AnthropicOfficial
Iterate on the Cardputer-Adv MicroPython app bundle (Claude Buddy, Snake, Hello) after the device is already provisioned via m5-onboard. Use when the user wants to add a new app, push a single changed .py without re-flashing, watch device serial logs, or run a one-shot REPL command. Trigger on "add an app", "push to the cardputer", "tail the device", "run on the device", or follow-up work after /maker-setup.
Software developmentClaude CodeClaude appsOpenAI Codex+14 moreApache-2.0GitHub stars of the source repository46.3k
Typescript Pro
Seth Hobson· Community
An expert TypeScript persona focused on advanced typing, enterprise architecture, and strict type safety.
Software developmentClaude CodeMITGitHub stars of the source repository42.3k
Scala Pro
Seth Hobson· Community
An expert Scala engineer persona specializing in functional programming, distributed systems, and enterprise-grade architecture.
Software developmentClaude CodeMITGitHub stars of the source repository42.3k
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download cuopt-numerical-optimization-api.zip