Structured Total Least Norm for Nonlinear Problems

DOI10.1137/S0895479896301662zbMath0930.65070OpenAlexW1990020994MaRDI QIDQ4243490

Haesun Park, John Glick, J. Ben Rosen

Publication date: 18 May 1999

Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/s0895479896301662

zbMATH Keywords

convergenceparameter estimationdata fittingfrequency estimationlinear predictionGauss-Newton methodtotal least normnonlinear structured approximation problemstructured nonlinear total least norm algorithm

Mathematics Subject Classification ID

Numerical smoothing, curve fitting (65D10) Numerical mathematical programming methods (65K05) Quadratic programming (90C20) Numerical computation of solutions to systems of equations (65H10)

Related Items (13)

Polynomial computations for blind image deconvolution ⋮ A quadratically convergent algorithm for structured low-rank approximation ⋮ A parameter estimation method for model analysis ⋮ A structure-preserving matrix method for the deconvolution of two Bernstein basis polynomials ⋮ A non-linear structure-preserving matrix method for the computation of the coefficients of an approximate greatest common divisor of two Bernstein polynomials ⋮ The Sylvester Resultant Matrix and Image Deblurring ⋮ Structured low-rank approximation for nonlinear matrices ⋮ The computation of multiple roots of a polynomial ⋮ An approximate factorisation of three bivariate Bernstein basis polynomials defined in a triangular domain ⋮ Total least norm solution for linear structured EIV model ⋮ A non-linear structure preserving matrix method for the low rank approximation of the Sylvester resultant matrix ⋮ Computing lower rank approximations of matrix polynomials ⋮ The Sylvester and Bézout resultant matrices for blind image deconvolution

This page was built for publication: Structured Total Least Norm for Nonlinear Problems