Low rank approximation of a Hankel matrix by structured total least norm
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Publication:1963917
DOI10.1023/A:1022347425533zbMath0944.65043OpenAlexW191724716MaRDI QIDQ1963917
J. Ben Rosen, Lei Zhang, Haesun Park
Publication date: 3 February 2000
Published in: BIT (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1023/a:1022347425533
algorithmconstrained optimizationnumerical examplessingular value decompositionperformancesrank reductionoverdetermined linear systemHankel structurestructured total least normstructure preserving low rank approximation
Related Items (15)
Structured low rank approximation ⋮ A quadratically convergent algorithm for structured low-rank approximation ⋮ A class of weighted low rank approximation of the positive semidefinite Hankel matrix ⋮ Novel alternating update method for low rank approximation of structured matrices ⋮ Solving Hankel matrix approximation problem using semidefinite programming ⋮ Enhanced image approximation using shifted rank-1 reconstruction ⋮ Dynamic behavior analysis via structured rank minimization ⋮ Stochastic algorithms for solving structured low-rank matrix approximation problems ⋮ Optimization challenges in the structured low rank approximation problem ⋮ A study of singular spectrum analysis with global optimization techniques ⋮ Total least norm solution for linear structured EIV model ⋮ A class of multilevel structured low-rank approximation arising in material processing ⋮ Approximate factorization of multivariate polynomials using singular value decomposition ⋮ A gradient system approach for Hankel structured low-rank approximation ⋮ Low rank approximation of the symmetric positive semidefinite matrix
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