Algorithm 937
DOI10.1145/2527267zbMath1305.65117arXiv1301.2707OpenAlexW2004789423WikidataQ34350266 ScholiaQ34350266MaRDI QIDQ5498691
Michael A. Saunders, Sou-Cheng T. Choi
Publication date: 10 February 2015
Published in: ACM Transactions on Mathematical Software (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1301.2707
sparse matrixlinear equationsill-posed problemKrylov subspace methodregressionLanczos processconjugate-gradient methodpseudoinverse solutionminimum-residual methoddata encapsulationsingular least-squares
Computational methods for sparse matrices (65F50) Numerical solutions to overdetermined systems, pseudoinverses (65F20) Iterative numerical methods for linear systems (65F10)
Related Items
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- The university of Florida sparse matrix collection
- MINRES-QLP: A Krylov Subspace Method for Indefinite or Singular Symmetric Systems
- LSMR: An Iterative Algorithm for Sparse Least-Squares Problems
- The Lanczos Algorithm With Partial Reorthogonalization
- GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems
- LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
- Solution of Sparse Indefinite Systems of Linear Equations
- Error Analysis of the Lanczos Algorithm for Tridiagonalizing a Symmetric Matrix
- The QLP Approximation to the Singular Value Decomposition
- Rank-Deficient and Discrete Ill-Posed Problems
- Writing Scientific Software
- Algorithm 937
- Methods of conjugate gradients for solving linear systems