arXiv (All)
open access
Geometric-resource bounds and permutation-averaged success probabilities for the WZP algorithm
Solving linear systems of equations is a fundamental task in machine learning and optimization. In this work, we extend the original WZP algorithm from real symmetric linear systems to complex Hermitian linear systems. We then study the success probability of the modified WZP algorithm from a resour
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Linear Algebra and Optimization with Applications to Machine Learning - Volume I: Linear Algebra for Computer Vision, Robotics, and Machine Learning#489444
- Linear Algebra and Optimization with Applications to Machine Learning - Volume II: Fundamentals of Optimization Theory with Applications to Machine Learning#489445
- A new analysis of the randomly pivoted Cholesky algorithm#659195
- A new analysis of the randomly pivoted Cholesky algorithm#657059
- A method for the solution of certain non-linear problems in least squares#6874
Record · ID 1355129
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive