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Cuopt Numerical Optimization API

NVIDIAOfficialCreated by NVIDIA cuOpt Team

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  • Tool integration
  • Software development
  • Apache-2.0
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From the author

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 description by NVIDIA cuOpt Team

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

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/cuopt-numerical-optimization-api/download
  2. Unzip 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:

InterfaceWhen to useReference
PythonUser is writing Python codereferences/python_api.md
C / C++User is embedding in a C/C++ applicationreferences/c_api.md
CLIUser is solving from MPS files on the command linereferences/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 continuousLP
LinearSome integer or binaryMILP
Has squared (x*x) or cross (x*y) termsContinuous (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*x or x*y terms (portfolio optimization, least squares, regularized regression).

Integer vs Continuous from Wording

Problem wording / conceptVariable typeExamples
Discrete entities (counts)INTEGERWorkers, cars, trucks, machines, pilots, facilities, units to manufacture
Yes/no or on/offINTEGER (binary, lb=0 ub=1)Open a facility, run a machine, assign a person to a shift
Amounts that can be fractionalCONTINUOUSTonnes, litres, dollars, hours, kWh, proportion of capacity
Rates or fractionsCONTINUOUSUtilization, 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)

ProblemLikely causeFix
InfeasibleConflicting constraintsCheck constraint logic and bounds
UnboundedMissing boundsAdd variable bounds
Slow solveLarge problemSet time limit; increase gap tolerance
QP rejected with MAXIMIZEQP only supports MINIMIZENegate the objective; negate the result
QP returns non-optimalQ not PSD or badly scaledCheck Q is PSD; rescale variables

Solver Settings (concepts)

SettingPurpose
time_limitStop after N seconds
mip_relative_gapStop MILP when within X% of optimal
mip_absolute_toleranceAbsolute MIP gap stop
log_to_consoleEnable solver logging

Syntax varies by interface — see the interface reference file.

Files

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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

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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)
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