Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview

Substantial progress has been made recently on developing provably accurate and efficient algorithms for low-rank matrix factorization via nonconvex optimization. While conventional wisdom often takes a dim view of nonconvex optimization algorithms due to their susceptibility to spurious local minim...

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Vydáno v:	IEEE transactions on signal processing Ročník 67; číslo 20; s. 5239 - 5269
Hlavní autoři:	Yuejie Chi, Lu, Yue M., Yuxin Chen
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 15.10.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Complexity theory Computational modeling Estimation Factorization First-order methods Iterative methods landscape analysis Machine learning matrix factorization nonconvex optimization Optimization Phase retrieval Principal components analysis Signal processing algorithms Statistical models statistics
ISSN:	1053-587X, 1941-0476
On-line přístup:	Získat plný text
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