Revisiting a Fast Newton Solver for a 2-D Spectral Estimation Problem: Computations with the Full Hessian

Fuente: arXiv
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Auteurs principaux: Cheng, Ji, Zhu, Bin
Format: Preprint
Publié: 2026
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author Cheng, Ji
Zhu, Bin
author_facet Cheng, Ji
Zhu, Bin
contents Spectral estimation plays a fundamental role in frequency-domain identification and related signal processing problems. This paper revisits a 2-D spectral estimation problem formulated in terms of convex optimization. More precisely, we work with the dual optimization problem and show that the full Hessian of the dual function admits a Toeplitz-block Toeplitz structure which is consistent with our finding in a previous work. This particular structure of the Hessian enables a fast inversion algorithm in the solution of the dual optimization problem via Newton's method whose superior speed of convergence is illustrated via simulations.
format Preprint
id arxiv_https___arxiv_org_abs_2601_02690
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Revisiting a Fast Newton Solver for a 2-D Spectral Estimation Problem: Computations with the Full Hessian
Cheng, Ji
Zhu, Bin
Optimization and Control
Spectral estimation plays a fundamental role in frequency-domain identification and related signal processing problems. This paper revisits a 2-D spectral estimation problem formulated in terms of convex optimization. More precisely, we work with the dual optimization problem and show that the full Hessian of the dual function admits a Toeplitz-block Toeplitz structure which is consistent with our finding in a previous work. This particular structure of the Hessian enables a fast inversion algorithm in the solution of the dual optimization problem via Newton's method whose superior speed of convergence is illustrated via simulations.
title Revisiting a Fast Newton Solver for a 2-D Spectral Estimation Problem: Computations with the Full Hessian
topic Optimization and Control
url https://arxiv.org/abs/2601.02690