Deep solution operators for variational inequalities via proximal neural networks

DOI10.1007/s40687-022-00327-1OpenAlexW4281714507WikidataQ114218941 ScholiaQ114218941MaRDI QIDQ2146915

Andreas Stein, Christoph Schwab

Publication date: 22 June 2022

Published in: Research in the Mathematical Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s40687-022-00327-1

Mathematics Subject Classification ID

Operator theory (47-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)

Related Items (1)

Reliable extrapolation of deep neural operators informed by physics or sparse observations

Uses Software

• Adam
• MgNet
• DeepONet

Cites Work

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• Parseval proximal neural networks
• Convex analysis and nonlinear optimization. Theory and examples.
• Convergence rate of some hybrid multigrid methods for variational inequalities
• Computational methods for quantitative finance. Finite element methods for derivative pricing
• Exponential ReLU DNN expression of holomorphic maps in high dimension
• Deep neural network structures solving variational inequalities
• Error bounds for approximations with deep ReLU networks
• MgNet: a unified framework of multigrid and convolutional neural network
• On the number of solutions to the complementarity problem and spanning properties of complementary cones
• Variationally consistent discretization schemes and numerical algorithms for contact problems
• The Primal-Dual Active Set Strategy as a Semismooth Newton Method
• An Introduction to Variational Inequalities and Their Applications
• An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
• On Noncoercive Variational Inequalities
• Deep optimal stopping
• Convex analysis and monotone operator theory in Hilbert spaces

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