SOLVING SPARSE LEAST SQUARES PROBLEMS WITH PRECONDITIONED CGLS METHOD ON PARALLEL DISTRIBUTED MEMORY COMPUTERS

DOI10.1080/01495739908947371zbMath0928.68139OpenAlexW2067391580MaRDI QIDQ4242704

Tianruo Yang, Hai-Xiang Lin

Publication date: 9 January 2000

Published in: Parallel Algorithms and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01495739908947371

zbMATH Keywords

incomplete modified Gram-Schmidt preconditionerPCGLS

Mathematics Subject Classification ID

Distributed algorithms (68W15)

Cites Work

• Algorithms and experiments for structural mechanics on high-performance architectures
• Reducing the effect of global communication in \(\text{GMRES} (m)\) and CG on parallel distributed memory computers
• Sparse Orthogonal Schemes for Structural Optimization Using the Force Method
• LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
• Further Development of a Primal-Dual Interior Point Method
• Accelerated projection methods for computing pseudoinverse solutions of systems of linear equations
• A Newton basis GMRES implementation
• Incomplete Methods for Solving $A^T Ax = b$
• Calculating the Singular Values and Pseudo-Inverse of a Matrix
• Methods of conjugate gradients for solving linear systems

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