Iterative methods for sparse linear systems
By Y. Saad. 2003
15,600 citations recorded by the source corpus.
Subfields: Atomic and Molecular Physics, and Optics, Computational Mechanics, Computational Theory and Mathematics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atomic and Molecular Physics, and Optics
- Valeria Simoncini
- Zhong-Zhi Bai
- Michele Benzi
- Daniel B. Szyld
- Yogi A. Erlangga
- Gene H. Golub
- Richard Szeliski
- Christine Tobler
In Computational Mechanics
- Ronald Fedkiw
- Thomas J. R. Hughes
- Peter Richtárik
- Yuri Bazilevs
- Frank Losasso
- Stanley J. Osher
- Frédéric Gibou
- Alfio Maria Quarteroni
In Computational Theory and Mathematics
- Valeria Simoncini
- Christine Tobler
- Daniel Kressner
- Peter Richtárik
- Michele Benzi
- Peter Benner
- Zhong-Zhi Bai
- Daniel B. Szyld
Citations in the corpus
- Data Encoding for Byzantine-Resilient Distributed Optimization (2019, methodology): “; (ii) iterative methods for solving sparse linear systems [60]; (iii) many graph algorithms, where the graph is represented by a fixed adjacency matrix, [61].”
- Should one compute the Temporal Difference fix point or minimize the Bellman Residual? The unified oblique projection view (2010, background): “We have discussed the close relations of our results with those of Schoknecht (2002) and Yu & Bertsekas (2008), and argued that our work simplifies and extends them.”
- INEXACT AND TRUNCATED PARAREAL-INTIME KRYLOV SUBSPACE METHODS FOR PARABOLIC OPTIMAL CONTROL PROBLEMS (2012, methodology): “1Truncated FOM is called IOM in [26] and it can be implemented in such a way that um = u0 + Vmxm is computed directly from um−1 without the need to store all the vectors in Vm.”
- Parallel Lagrange-Newton-Krylov-Schur Methods for PDE-Constrained Optimization. Part I: The Krylov-Schur Solver (2005, methodology): “The performance of a Krylov method depends highly on the preconditioned operator spectrum [21, 36].”
- Krylov Subspace Methods for Linear Systems with Tensor Product Structure (2010, neutral): “[21]).”
- A comparison of model reduction techniques from structural dynamics, numerical mathematics and systems and control (2013, methodology): “This can be achieved using e.g. Arnoldi or Lanczos algorithms (explanation of these algorithms and implementation details are given in [60]).”
- Sourcebook of parallel computing (2003, background): “[564], Morton and Mayers [690], Quarteroni and Valli [784], Saad [820], and Smith et al.”
- GMRES, L-Curves, and Discrete Ill-Posed Problems (2002, methodology): “The GMRES method and the condition L-curve The GMRES method of Saad and Schultz [15] [16] is based on the Arnoldi process.”
- Interactive local adjustment of tonal values (2006, background): “The resulting factor matrix is then used as the preconditioner in all subsequent invocations of PCG.”
- X-ray tomographic and hydraulic characterization of burrowing by three earthworm species in repacked soil cores (2003, methodology): “This system of equations was solved by a classical Cholesky decomposition (Saad, 1996).”