Data-Driven Mirror Descent with Input-Convex Neural Networks

DOI10.1137/22m1508613arXiv2206.06733OpenAlexW4382056236MaRDI QIDQ6171691

Junqi Tang, Unnamed Author, Subhadip Mukherjee, Carola-Bibiane Schönlieb

Publication date: 14 August 2023

Published in: SIAM Journal on Mathematics of Data Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2206.06733

zbMATH Keywords

inverse problemsmirror descentdata-driven convex optimization solversinput-convex neural networks

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Learning and adaptive systems in artificial intelligence (68T05) Roundoff error (65G50) Applications of functional analysis in optimization, convex analysis, mathematical programming, economics (46N10)

Cites Work

• Gradient methods for minimizing composite functions
• An optimal method for stochastic composite optimization
• A generalized online mirror descent with applications to classification and regression
• An optimal randomized incremental gradient method
• Mirror descent and nonlinear projected subgradient methods for convex optimization.
• A first-order primal-dual algorithm for convex problems with applications to imaging
• Accelerated Bregman proximal gradient methods for relatively smooth convex optimization
• The Ordered Subsets Mirror Descent Optimization Method with Applications to Tomography
• Variational Analysis
• Relatively Smooth Convex Optimization by First-Order Methods, and Applications
• Data-Driven Nonsmooth Optimization
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