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.
Description:
Model and solve LP, MILP, and QP problems using NVIDIA cuOpt's GPU-accelerated solver via Python, C, and CLI interfaces.
This skill is ready for commercial/non-commercial use.
Owner
NVIDIA
License/Terms of Use:
Apache 2.0
Use Case:
Developers and engineers solving linear programming, mixed-integer linear programming, and quadratic programming optimization problems using NVIDIA cuOpt across Python, C, and CLI interfaces.
Deployment Geography for Use:
Global
Requirements / Dependencies:
Requires API Key or External Credential: [Not Specified]
Credential Type(s): [None identified]
Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate.
Known Risks and Mitigations:
Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills.
Mitigation: Review and scan skill before deployment.
Reference(s):
- Python API Reference
- C API Reference
- CLI API Reference
- QP Examples
- cuOpt User Guide
- Third-Party Modeling Languages
Skill Output:
Output Type(s): [Code, Shell commands, Configuration instructions, Analysis]
Output Format: [Markdown with inline code blocks]
Output Parameters: [1D]
Other Properties Related to Output: [None]
Evaluation Agents Used:
- Claude Code (
aws/anthropic/bedrock-claude-opus-4-8) - Codex (
openai/openai/gpt-5.5)
Evaluation Tasks:
9 evaluation tasks (9 positive) in isolated sandbox pods.
Evaluation Metrics Used:
Reported benchmark dimensions:
- Security: Whether the skill is safe to use (unsafe operations, secret leakage, unauthorized access).
- Correctness: Whether the final answer is correct against the reference answer.
- Discoverability: Whether the right skill was found and executed when needed.
- Effectiveness: Whether the skill helps complete the user's goal and expected workflow.
- Efficiency: Whether the skill avoids wasted tool or skill usage.
Underlying evaluation signals used in this run:
security: Checks for unsafe operations, secret leakage, and unauthorized access.skill_execution: Whether the expected skill was found and executed.skill_efficiency: Routing quality, workspace-aware skill reads, and productive tool use.accuracy: Final-answer correctness against the reference answer.goal_accuracy: Whether the user's goal was achieved.behavior_check: Whether the expected workflow behavior was followed.
Evaluation Results:
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 59% → 97% (+39 points) | 61% → 93% (+32 points) |
| Security | 100% → 100% (±0 points) | 100% → 100% (±0 points) |
| Correctness | 87% → 98% (+11 points) | 91% → 100% (+9 points) |
| Discoverability | 28% → 100% (+72 points) | 44% → 92% (+48 points) |
| Effectiveness | 69% → 89% (+20 points) | 68% → 81% (+12 points) |
| Efficiency | 10% → 100% (+90 points) | 0% → 90% (+90 points) |
Testing Completed:
[x] Agent Red-Teaming
[ ] Network Security
[ ] Product Security
Skill Version(s):
26.10.00 (source: frontmatter)
Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
(For Release on NVIDIA Platforms Only)
Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.