Optimal norms and the computation of joint spectral radius of matrices
โ Scribed by Mohsen Maesumi
- Publisher
- Elsevier Science
- Year
- 2008
- Tongue
- English
- Weight
- 863 KB
- Volume
- 428
- Category
- Article
- ISSN
- 0024-3795
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โฆ Synopsis
The notion of spectral radius of a set of matrices is a natural extension of spectral radius of a single matrix. The finiteness conjecture (FC) claims that among the infinite products made from the elements of a given finite set of matrices, there is a certain periodic product, made from the repetition of the optimal product, whose rate of growth is maximal. FC has been disproved. In this paper it is conjectured that FC is almost always true, and an algorithm is presented to verify the optimality of a given product. The algorithm uses optimal norms, as a special subset of extremal norms. Several conjectures related to optimal norms and nondecomposable sets of matrices are presented. The algorithm has successfully calculated the spectral radius of several parametric families of pairs of matrices associated with compactly supported multi-resolution analyses and wavelets. The results of related numerical experiments are presented.
๐ SIMILAR VOLUMES
Let M + n be the set of entrywise nonnegative n ร n matrices. Denote by r(A) the spectral radius (Perron root) of A โ M + n . Characterization is obtained for maps f : In particular, it is shown that such a map has the form for some S โ M + n with exactly one positive entry in each row and each co
## We prove the spectral radius inequality ฯ(A for nonnegative matrices using the ideas of Horn and Zhang. We obtain the inequality A โข B ฯ(A T B) for nonnegative matrices, which improves Schur's classical inequality , where โข denotes the spectral norm. We also give counterexamples to two conject
The joint spectral radius of a set of matrices is a measure of the maximal asymptotic growth rate that can be obtained by forming long products of matrices taken from the set. This quantity appears in a number of application contexts but is notoriously difficult to compute and to approximate. We int