Higham, Nicholas J. and Relton, Samuel D. (2016) Estimating the Largest Elements of a Matrix. SIAM Journal on Scientific Computing, 38 (5). C584C601.
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Abstract
We derive an algorithm for estimating the largest $p \geq 1$ values $a_{ij}$ or $a_{ij}$ for an $m \times n$ matrix $A$, along with their locations in the matrix. The matrix is accessed using only matrixvector or matrixmatrix products. For p = 1 the algorithm estimates the norm $\A\_M := \max_{i,j} a_{ij}$ or $\max_{i,j} a_{ij}$. The algorithm is based on a power method for mixed subordinate matrix norms and iterates on $n \times t$ matrices, where $t \geq p$ is a parameter. For p = t = 1 we show that the algorithm is essentially equivalent to rook pivoting in Gaussian elimination; we also obtain a bound for the expected number of matrixvector products for random matrices and give a class of counterexamples. Our numerical experiments show that for p = 1 the algorithm usually converges in just two iterations, requiring the equivalent of 4t matrixvector products, and for t = 2 the algorithm already provides excellent estimates that are usually within a factor 2 of the largest element and frequently exact. For p > 1 we incorporate deflation to improve the performance of the algorithm. Experiments on reallife datasets show that the algorithm is highly effective in practice. Read More: http://epubs.siam.org/doi/abs/10.1137/15M1053645
Item Type:  Article 

Uncontrolled Keywords:  matrix norm estimation, largest elements, power method, mixed subordinate norm, condition number estimation 
Subjects:  MSC 2010, the AMS's Mathematics Subject Classification > 65 Numerical analysis 
Depositing User:  Dr Samuel Relton 
Date Deposited:  28 Oct 2016 
Last Modified:  20 Oct 2017 14:13 
URI:  https://eprints.maths.manchester.ac.uk/id/eprint/2510 
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Estimating the Largest Elements of a Matrix. (deposited 21 Dec 2015)

Estimating the Largest Elements of a Matrix. (deposited 18 Aug 2016)
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Estimating the Largest Elements of a Matrix. (deposited 18 Aug 2016)
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