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pro vyhledávání: '"Proximal algorithm"'
This paper considers a consensus optimization problem, where all the nodes in a network, with access to the zeroth-order information of its local objective function only, attempt to cooperatively achieve a common minimizer of the sum of their local o
Externí odkaz:
http://arxiv.org/abs/2406.09816
Publikováno v:
Carpathian Journal of Mathematics, 2024 Jan 01. 40(1), 65-76.
Externí odkaz:
https://www.jstor.org/stable/27259297
This paper is devoted to general nonconvex problems of multiobjective optimization in Hilbert spaces. Based on Mordukhovich's limiting subgradients, we define a new notion of Pareto critical points for such problems, establish necessary optimality co
Externí odkaz:
http://arxiv.org/abs/2403.09922
Autor:
Chihara, Ryohei
We sketch an application of proximal algorithms to the deformation of de Rham currents into cycles, which is presented as a convex optimization problem. Emphasis is placed on the use of total variation denoising for differential forms, specifically i
Externí odkaz:
http://arxiv.org/abs/2312.11304
Akademický článek
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Autor:
Junior, Valdines Leite de Sousa, de Meireles, Lucas Vidal, Lima, Samara Costa, Silva, Gilson do Nascimento
Since introduced by Martinet and Rockafellar, the proximal point algorithm was generalized in many fruitful directions. More recently, in 2002, Pennanen studied the proximal point algorithm without monotonicity. A year later, Iusem and Svaiter joined
Externí odkaz:
http://arxiv.org/abs/2308.02014
As a popular channel pruning method for convolutional neural networks (CNNs), network slimming (NS) has a three-stage process: (1) it trains a CNN with $\ell_1$ regularization applied to the scaling factors of the batch normalization layers; (2) it r
Externí odkaz:
http://arxiv.org/abs/2307.00684
This work presents PANTR, an efficient solver for nonconvex constrained optimization problems, that is well-suited as an inner solver for an augmented Lagrangian method. The proposed scheme combines forward-backward iterations with solutions to trust
Externí odkaz:
http://arxiv.org/abs/2306.17119
In this paper, we develop a distributed mixing-accelerated primal-dual proximal algorithm, referred to as MAP-Pro, which enables nodes in multi-agent networks to cooperatively minimize the sum of their nonconvex, smooth local cost functions in a dece
Externí odkaz:
http://arxiv.org/abs/2304.02830
Autor:
László, Szilárd Csaba
This paper deals with an inertial proximal algorithm that contains a Tikhonov regularization term, in connection to the minimization problem of a convex lower semicontinuous function $f$. We show that for appropriate Tikhonov regularization parameter
Externí odkaz:
http://arxiv.org/abs/2302.02115