Disciplined Convex Programming
A guide to constructing optimization expressions with known signs and curvature under disciplined convex programming rules.
External resource. No execution or independent verification is claimed here.
Can a small suite distinguish mathematically equivalent formulations that are accepted or rejected by the stated DCP rules?
An evaluator independently derives sign and curvature for each expression; syntax acceptance is reported separately from numerical feasibility or optimality.
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 Convex Programming. https://www.cvxpy.org/tutorial/dcp/index.html.
Rights need review. Review the scope and upstream conditions before reuse.
project source · Apache-2.0
The pinned CVXPY repository snapshot carries Apache-2.0 terms, which discuss code and documentation source. The unversioned documentation site's deployed revision was not established, so this does not assert that the fetched page is fully licensed by that snapshot.
Inspect the license evidence ↗Still unresolved
- Third-party figures, linked papers, datasets and dependency licenses have not been assessed; no external content is copied into this catalog.
- The fetched documentation page has no established deployed version; its correspondence to the pinned project-license snapshot remains unresolved.