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[BOK][B] Variational analysis and applications
BS Mordukhovich - 2018 - Springer
Boris S. Mordukhovich Page 1 Springer Monographs in Mathematics Boris S. Mordukhovich
Variational Analysis and Applications Page 2 Springer Monographs in Mathematics Editors-in-Chief …
Variational Analysis and Applications Page 2 Springer Monographs in Mathematics Editors-in-Chief …
Golden ratio algorithms for variational inequalities
Y Malitsky - Mathematical Programming, 2020 - Springer
The paper presents a fully adaptive algorithm for monotone variational inequalities. In each
iteration the method uses two previous iterates for an approximation of the local Lipschitz …
iteration the method uses two previous iterates for an approximation of the local Lipschitz …
Iterated function systems: A comprehensive survey
arxiv:2211.14661v1 [math.PR] 26 Nov 2022 Page 1 Iterated Function Systems: A
Comprehensive Survey Ramen Ghosh ∗ Jakub Mareček † November 29, 2022 Abstract We …
Comprehensive Survey Ramen Ghosh ∗ Jakub Mareček † November 29, 2022 Abstract We …
Convolutional proximal neural networks and plug-and-play algorithms
In this paper, we introduce convolutional proximal neural networks (cPNNs), which are by
construction averaged operators. For filters with full length, we propose a stochastic gradient …
construction averaged operators. For filters with full length, we propose a stochastic gradient …
The Douglas–Rachford algorithm for convex and nonconvex feasibility problems
Abstract The Douglas–Rachford algorithm is an optimization method that can be used for
solving feasibility problems. To apply the method, it is necessary that the problem at hand is …
solving feasibility problems. To apply the method, it is necessary that the problem at hand is …
[PDF][PDF] Phase retrieval, what's new
DR Luke - SIAG/OPT Views and News, 2017 - researchgate.net
Ask an engineer to solve a problem and she will come back in a day or so with something
that seems to work well enough most of the time. Ask a mathematician to solve the same …
that seems to work well enough most of the time. Ask a mathematician to solve the same …
A minimalist approach to 3D photoemission orbital tomography: algorithms and data requirements
Photoemission orbital tomography provides direct access from laboratory measurements to
the real-space molecular orbitals of well-ordered organic semiconductor layers. Specifically …
the real-space molecular orbitals of well-ordered organic semiconductor layers. Specifically …
Variational analysis perspective on linear convergence of some first order methods for nonsmooth convex optimization problems
We study linear convergence of some first-order methods such as the proximal gradient
method (PGM), the proximal alternating linearized minimization (PALM) algorithm and the …
method (PGM), the proximal alternating linearized minimization (PALM) algorithm and the …
Optimization on spheres: models and proximal algorithms with computational performance comparisons
We present a unified treatment of the abstract problem of finding the best approximation
between a cone and spheres in the image of affine transformations. Prominent instances of …
between a cone and spheres in the image of affine transformations. Prominent instances of …
Necessary conditions for linear convergence of iterated expansive, set-valued map**s
We present necessary conditions for monotonicity of fixed point iterations of map**s that
may violate the usual nonexpansive property. Notions of linear-type monotonicity of fixed …
may violate the usual nonexpansive property. Notions of linear-type monotonicity of fixed …