Solving Hankel matrix approximation problem using semidefinite programming

DOI10.1016/j.cam.2006.02.033zbMath1117.65083OpenAlexW1977701268MaRDI QIDQ875374

Suliman Al-Homidan

Publication date: 13 April 2007

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2006.02.033

zbMATH Keywords

algorithmsemidefinite programmingnumerical examplesHankel matrixnearest matrixprimal-dual interior-point algorithm

Mathematics Subject Classification ID

Related Items (3)

Slant Hankel operators of multivariate order ⋮ Minimal condition number for positive definite Hankel matrices using semidefinite programming ⋮ Projection Methods in Conic Optimization

Uses Software

• LSQR
• SDPT3
• KELLEY
• CRAIG

Cites Work

• Solving semidefinite-quadratic-linear programs using SDPT3
• A successive projection method
• Smallest eigenvalues of Hankel matrices for exponential weights
• Hybrid methods for approximating Hankel matrix
• Low rank approximation of a Hankel matrix by structured total least norm
• On the factorization of matrices
• Sizing and Least-Change Secant Methods
• An Algorithm for Restricted Least Squares Regression
• LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
• The role of abstract algebra in structured estimation theory
• On the Nesterov--Todd Direction in Semidefinite Programming
• The Solution to a Structured Matrix Approximation Problem Using GrassmanCoordinates
• Solving semidefinite programs using preconditioned conjugate gradients
• Functional Operators (AM-22), Volume 2
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