Estimating the Largest Elements of a Matrix

DOI10.1137/15M1053645zbMath1352.65132MaRDI QIDQ2830567

Nicholas J. Higham, Samuel D. Relton

Publication date: 28 October 2016

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

zbMATH Keywords

algorithmnumerical experimentsrandom matricesGaussian eliminationpower methodmatrix-vector productscondition number estimationlargest elementsmatrix norm estimationmixed subordinate norm

Mathematics Subject Classification ID

Numerical computation of matrix norms, conditioning, scaling (65F35)

Related Items (4)

\(t\)-copula from the viewpoint of tail dependence matrices ⋮ Sensitivity of Matrix Function Based Network Communicability Measures: Computational Methods and A Priori Bounds ⋮ Implementation of an interior point method with basis preconditioning ⋮ The global convergence of the nonlinear power method for mixed-subordinate matrix norms

Uses Software

• LAPACK
• SparseMatrix
• mctoolbox
• Algorithm 674
• mftoolbox
• SONEST
• CONEST
• MovieLens
• LEMP

Cites Work

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• Communicability graph and community structures in complex networks
• Convergence of a subgradient method for computing the bound norm of matrices
• The power method for l\(^p\) norms
• Corrigendum: The growth factor and efficiency of Gaussian elimination with rook pivoting
• The growth factor and efficiency of Gaussian elimination with rook pivoting
• Strong rank revealing Cholesky factorization
• Comparison of software for computing the action of the matrix exponential
• Efficient and Stable Arnoldi Restarts for Matrix Functions Based on Quadrature
• Convex Optimization in Normed Spaces
• The university of Florida sparse matrix collection
• Network Properties Revealed through Matrix Functions
• Computing the Action of the Matrix Exponential, with an Application to Exponential Integrators
• Condition Estimates
• FORTRAN codes for estimating the one-norm of a real or complex matrix, with applications to condition estimation
• LAPACK Users' Guide
• A Block Algorithm for Matrix 1-Norm Estimation, with an Application to 1-Norm Pseudospectra
• Experience with a Matrix Norm Estimator
• Accuracy and Stability of Numerical Algorithms
• Subspace Iteration Randomization and Singular Value Problems
• Functions of Matrices

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