Linearly Constrained Nonsmooth Optimization for Training Autoencoders

DOI10.1137/21M1408713zbMath1497.90155arXiv2103.16232OpenAlexW3150868911MaRDI QIDQ5097018

Xin Liu, Xiaojun Chen, Wei Liu

Publication date: 19 August 2022

Published in: SIAM Journal on Optimization (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2103.16232

zbMATH Keywords

neural networkpenalty methodsmoothing approximationautoencodersfinite-sum optimization

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Nonlinear programming (90C30)

Cites Work

• Smoothing methods for nonsmooth, nonconvex minimization
• Auto-association by multilayer perceptrons and singular value decomposition
• The subdifferential of measurable composite max integrands and smoothing approximation
• Optimization for deep learning: an overview
• Reducing the Dimensionality of Data with Neural Networks
• Semismooth and Semiconvex Functions in Constrained Optimization
• MultiComposite Nonconvex Optimization for Training Deep Neural Networks
• Local Linear Convergence of the Alternating Direction Method of Multipliers on Quadratic or Linear Programs
• Penalty Methods for a Class of Non-Lipschitz Optimization Problems
• A logical calculus of the ideas immanent in nervous activity
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