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Correct dual-value scope to LP/QP in numerical-optimization API skills #1408

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rapids-bot merged 3 commits into from
Jun 10, 2026
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Correct dual-value scope to LP/QP in numerical-optimization API skills #1408

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Correct dual-value scope to LP/QP in numerical-optimization API skills
cafzal Jun 8, 2026
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Address review: remove duplicate Duals note from api-c assets/README
cafzal Jun 9, 2026
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Address review: minimize per reviewer feedback
cafzal Jun 9, 2026
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4 changes: 4 additions & 0 deletions skills/cuopt-numerical-optimization-api-c/SKILL.md
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Expand Up @@ -35,6 +35,10 @@ QP uses the same library, include/lib paths, and build pattern as LP/MILP — on
- **Continuous variables only** — set `CUOPT_CONTINUOUS` for every variable; integer QP is not supported.
- **Q should be PSD** for a convex problem.

## Dual values (LP / QP)

`cuOptGetDualSolution` and `cuOptGetReducedCosts` return duals and reduced costs for **LP and QP**. They are not returned for a problem with quadratic constraints (the arrays are filled with `NaN`), so read them only when all constraints are linear. See [assets/lp_duals](assets/lp_duals/) for the call sequence.

## Debugging (MPS / C)

**MPS parsing:** Required sections in order: NAME, ROWS, COLUMNS, RHS, (optional) BOUNDS, ENDATA. Integer markers: `'MARKER'`, `'INTORG'`, `'INTEND'`.
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9 changes: 5 additions & 4 deletions skills/cuopt-numerical-optimization-api-python/SKILL.md
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Expand Up @@ -253,13 +253,14 @@ settings.set_parameter("log_to_console", 1)
| QP rejected with MAXIMIZE | QP only supports MINIMIZE | Negate the objective: minimize `-f(x)` |
| QP returns non-optimal | Q not PSD or variables badly scaled | Check Q is PSD; rescale variables to similar magnitudes |

## Getting Dual Values (LP only)
## Getting Dual Values (LP / QP)

Duals and reduced costs are returned for **LP and QP**. They are not returned for a problem with quadratic constraints (every value comes back as `NaN`), so read them only when all constraints are linear. MILP returns no duals.

```python
if problem.Status.name == "Optimal":
constraint = problem.getConstraint("resource_a")
shadow_price = constraint.DualValue
print(f"Shadow price: {shadow_price}")
constraint = problem.getConstraint("resource_a") # linear constraint
print(f"Dual value: {constraint.DualValue}") # NaN if the model has quadratic constraints
```

## Reference Models
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