Riemannian Newton-CG methods for constructing a positive doubly stochastic matrix from spectral data*

DOI10.1088/1361-6420/abbac5zbMath1460.15023arXiv2006.13439OpenAlexW3087948276WikidataQ115293001 ScholiaQ115293001MaRDI QIDQ5132266

Zheng-Jian Bai, Yang Wang, Zhi Zhao

Publication date: 10 November 2020

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.13439

zbMATH Keywords

Newton's methodRiemannian manifoldinverse eigenvalue problempositive doubly stochastic matrix

Mathematics Subject Classification ID

Eigenvalues, singular values, and eigenvectors (15A18) Inverse problems in linear algebra (15A29) Stochastic matrices (15B51) Numerical solutions to inverse eigenvalue problems (65F18)

Related Items

A new constrained optimization model for solving the nonsymmetric stochastic inverse eigenvalue problem, On a numerical construction of doubly stochastic matrices with prescribed eigenvalues, A Riemannian inexact Newton dogleg method for constructing a symmetric nonnegative matrix with prescribed spectrum, A direct solution to the stochastic inverse eigenvalue problem for complex-valued eigenspectra, A Riemannian under-determined BFGS method for least squares inverse eigenvalue problems

Uses Software

• Matlab

Cites Work

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