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NVIDIAKnown publisherNVIDIA/cuopt/skills/cuopt-numerical-optimization-api/SKILL.md

cuopt-numerical-optimization-api

Skill1.1k starsChanged 5 months agoApache-2.0View on GitHub

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.

1 other project uses a version of this, 1 of them edited.

Adopted by

Steady: 0 new projects took it in the last 4 weeks, none the 4 weeks before.thin data

Week of Sep 13: 0 new projectsWeek of Sep 20: 0 new projectsWeek of Sep 27: 0 new projectsWeek of Oct 4: 0 new projectsWeek of Oct 11: 0 new projectsWeek of Oct 18: 0 new projectsWeek of Oct 25: 0 new projectsWeek of Nov 1: 0 new projects
Sep 2026Oct 2026Nov 2026
New projects taking a version of this file each week, over the 8 weeks our latest run read: only 0 in all, so this is a hint, not a trend.

Most recently taken up by

Who's talking about NVIDIA/cuopt

The biggest 5 of 45 mentions linking to the repository that our crawl found. Each line is their own headline.

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Talked about on GitHub (45 times).

On GitHub · 45

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The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 3 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    If the interface is not yet clear, ask before writing any code.

    line 34
  2. Distinctive:

    Use LP: when every quantity can meaningfully be fractional: flows, proportions, rates, dollars, hours, tonnes of material, etc.

    line 55
  3. Distinctive:

    Use MILP: when the problem mentions counts of discrete entities, yes/no choices, or either/or decisions (e.g.

    line 56
  4. Very specific:

    Use QP: when the objective minimizes variance, squared error, or any expression with x*x or x*y terms (portfolio optimization, least …

    line 57

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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.

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