A self-adaptive regularized alternating least squares method for tensor decomposition problems

DOI10.1142/S0219530519410057zbMath1432.90125OpenAlexW2982550955MaRDI QIDQ5220069

Xianpeng Mao, Yuning Yang, Gong Lin Yuan

Publication date: 10 March 2020

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

Full work available at URL: https://doi.org/10.1142/s0219530519410057

zbMATH Keywords

nonconvexblock coordinate descenttensor decompositionself-adaptivealternating least squares

Mathematics Subject Classification ID

Numerical solutions to overdetermined systems, pseudoinverses (65F20) Nonconvex programming, global optimization (90C26) Stability and convergence of numerical methods for boundary value problems involving PDEs (65N12) Rate of convergence, degree of approximation (41A25) Multilinear algebra, tensor calculus (15A69)

Related Items (1)

A simultaneous decomposition for three quaternion tensors with applications in color video signal processing

Uses Software

• Tensorlab

Cites Work

• Tensor Decompositions and Applications
• On accelerating the regularized alternating least-squares algorithm for tensors
• Some convergence results on the regularized alternating least-squares method for tensor decomposition
• Analysis of individual differences in multidimensional scaling via an \(n\)-way generalization of ``Eckart-Young decomposition
• A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion
• Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
• Tensor Decomposition for Signal Processing and Machine Learning
• A Trust-Region-Based Alternating Least-Squares Algorithm for Tensor Decompositions
• Convergence of the Iterates of Descent Methods for Analytic Cost Functions

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