scientific article; zbMATH DE number 7415112
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Publication:5159451
Hassan Rafique, Qihang Lin, Ming-rui Liu, Tianbao Yang
Publication date: 27 October 2021
Full work available at URL: https://arxiv.org/abs/1810.10207
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
variational inequalitymin-maxfirst-order convergencegenerative adversarial netsweakly-convex-weakly-concave
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Decentralized Gradient Descent Maximization Method for Composite Nonconvex Strongly-Concave Minimax Problems ⋮ Optimality Conditions for Nonsmooth Nonconvex-Nonconcave Min-Max Problems and Generative Adversarial Networks ⋮ Alternating Proximal-Gradient Steps for (Stochastic) Nonconvex-Concave Minimax Problems
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Cites Work
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- Accelerated gradient methods for nonconvex nonlinear and stochastic programming
- Solving strongly monotone variational and quasi-variational inequalities
- Robust linear least squares regression
- Finite-dimensional variational inequality and nonlinear complementarity problems: A survey of theory, algorithms and applications
- Monotone (nonlinear) operators in Hilbert space
- A first-order primal-dual algorithm for convex problems with applications to imaging
- An efficient primal dual prox method for non-smooth optimization
- On the convergence properties of non-Euclidean extragradient methods for variational inequalities with generalized monotone operators
- The proximal point method for nonmonotone variational inequalities
- On some non-linear elliptic differential functional equations
- Saddle-Point Dynamics: Conditions for Asymptotic Stability of Saddle Points
- Robust Stochastic Approximation Approach to Stochastic Programming
- Monotone Operators and the Proximal Point Algorithm
- A New Projection Method for Variational Inequality Problems
- Prox-Method with Rate of Convergence O(1/t) for Variational Inequalities with Lipschitz Continuous Monotone Operators and Smooth Convex-Concave Saddle Point Problems
- Most Tensor Problems Are NP-Hard
- Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming
- Extragradient Method with Variance Reduction for Stochastic Variational Inequalities
- Unified framework of extragradient-type methods for pseudomonotone variational inequalities.
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