Skip to content

Note dual/sensitivity support per problem type in formulation skill #1393

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
rapids-bot merged 2 commits into from
Jun 5, 2026
Merged

Note dual/sensitivity support per problem type in formulation skill #1393

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
18dd23b
Note dual/sensitivity support per problem type in formulation skill
cafzal Jun 5, 2026
a401504
Address #1355-style review feedback: drop beta label and API specifics
cafzal Jun 5, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 3 additions & 0 deletions skills/cuopt-numerical-optimization-formulation/SKILL.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -32,9 +32,12 @@ Concepts and workflow for going from a problem description to a clear formulatio
| Constraints | Linear | Linear | Linear (no quadratic constraints) |
| Variables | Continuous | Mixed: continuous + integer/binary | Continuous |
| Sense | min or max | min or max | **minimize only** (negate to max) |
| Duals / sensitivity | Shadow prices + reduced costs | **None** (integer optima) | Shadow prices + reduced costs |

If the objective is purely linear, prefer LP/MILP — do not artificially introduce quadratic terms. If any variable is integer or binary, the problem is MILP regardless of the rest.

**Post-solve sensitivity (LP / QP only).** Continuous LP and QP solutions expose **dual values** (shadow prices — the marginal objective change per unit a binding constraint is relaxed: *where to invest to improve the outcome*) and **reduced costs** (for a variable the optimizer left at zero, how far it must improve to enter the solution: a *near-miss*). **MILP solutions have no duals** — integer optima are not continuous, so there are none to return. See the language-specific API skills for how to retrieve them after a solve.

## Required formulation questions

Ask these if not already clear:
Expand Down