Linear Algebra (scipy.linalg)
A guide to dense linear systems, least squares, matrix factorizations, eigenproblems and matrix functions.
External resource. No execution or independent verification is claimed here.
How do residuals and solution errors differ as a known linear system becomes ill-conditioned?
An evaluator constructs matrices with controlled spectra and known solutions, recomputes residuals and separates conditioning from implementation error.
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
- SciPy 1.18.0
- Compatible NumPy and native numerical-library build; exact environment not prepared
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
SciPy documentation. Linear Algebra (scipy.linalg). https://docs.scipy.org/doc/scipy-1.18.0/tutorial/linalg.html; observed version 1.18.0.
Catalog listing reviewed. This review covers the description and source links displayed here.
Versioned SciPy 1.18.0 page and BSD-3-Clause documentation evidence reviewed.
Reviewed 2026-09-14. Copying or adapting source files remains subject to their own terms.
documentation · BSD-3-Clause
SciPy's versioned developer policy explicitly includes documentation under its default BSD license, subject to separately specified exceptions; the matching 1.18.0 LICENSE.txt identifies the three-clause terms.
Inspect the license evidence ↗Before copying source material
- Third-party figures, linked papers, datasets and dependency licenses have not been assessed; no external content is copied into this catalog.