Disciplined Parametrized Programming
A guide to parameterized convex programs, repeated canonicalization and differentiating solution maps.
External resource. No execution or independent verification is claimed here.
On a smooth, uniquely solved parameterized quadratic problem, do solution sensitivities agree with an analytic derivative?
The evaluator derives the closed-form solution and derivative, compares finite differences away from active-set changes and accounts separately for compilation and solve time.
What you could produce
- A versioned minimal protocol, evaluator-owned test cases, and a comparison report with numerical/statistical uncertainty and failures.
Before you use it
- CVXPY, compatible NumPy/SciPy and a suitable optimization solver; versions not pinned
Limits to keep in view
- No upstream example, source package, build hook or submitted code was executed.
- The current qualified pilot accepts only self-contained Python 3.13 with a 90-second deadline. This reference's package/runtime is not qualified for that path.
- The proposed protocol requires bounded resource estimates and an independently controlled evaluator before any scientific execution claim.
Source and permission context
CVXPY documentation. Disciplined Parametrized Programming. https://www.cvxpy.org/tutorial/dpp/index.html.
Catalog listing reviewed. This review covers the description and source links displayed here.
Unversioned CVXPY page and pinned Apache-2.0 project LICENSE reviewed.
Reviewed 2026-09-14. Copying or adapting source files remains subject to their own terms.
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