1,578 research outputs found
Stochastic Generalized Nash Equilibrium-Seeking in Merely Monotone Games
No full textWe solve the stochastic generalized Nash equilibrium (SGNE) problem in merely monotone games with expected value cost functions. Specifically, we present the first distributed SGNE-seeking algorithm for monotone games that require one proximal computation (e.g., one projection step) and one pseudogradient evaluation per iteration. Our main contribution is to extend the relaxed forward–backward operator splitting by the Malitsky (Mathematical Programming, 2019) to the stochastic case and in turn to show almost sure convergence to an SGNE when the expected value of the pseudogradient is approximated by the average over a number of random samples.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Sergio GrammaticoTeam Bart De Schutte
A distributed forward-backward algorithm for stochastic generalized Nash equilibrium seeking
No full textWe consider the stochastic generalized Nash equilibrium problem (SGNEP) with expected-value cost functions. Inspired by Yi and Pavel (2019), we propose a distributed generalized Nash equilibrium seeking algorithm based on the preconditioned forward-backward operator splitting for SGNEPs, where, at each iteration, the expected value of the pseudogradient is approximated via a number of random samples. Our main contribution is to show almost sure convergence of the proposed algorithm if the pseudogradient mapping is restricted (monotone and) cocoercive.Accepted Author ManuscriptTeam Bart De Schutte
La biblioteca di un grammatico
No full textRicostruire la biblioteca di un grammatico non è sempre impresa agevole, specie se il grammatico in questione, Giuniano Maio, in sintonia con una temperie culturale che enfatizzava la lettura diretta degli auctores e tuonava contro la fallace erudizione di compilatori medievali, amava dissimulare la presenza nel suo scriptorium di materiali non proprio à la page, per non incorrere negli strali di qualche integralista della valliana eleganza.Pubblicato a Napoli nel 1475, il lessico De priscorum proprietate verborum fu edito altre cinque volte, ben tre a Venezia. Il volume di Palumbo ne ricostruisce le fonti, soffermandosi sul sospetto di plagio che grava sull’opera, ed esamina la fortuna della pregevole compilazione
CDKN2A novel mutation in a patient from a melanoma-prone family
No full textCDKN2A Is thought to be the main candidate gene for melanoma susceptibility. Deletion or mutations in the CDKN2A gene may produce an imbalance between functional p16 and cyclin D, causing abnormal cell growth. We here describe a novel mutation consisting of a 1 bp deletion at nucleotide position 201 (codon 67) (CACGGcGCG) resulting in a truncated protein (stop codon 145). The patient, a female subject from a melanoma-prone family, presented at the age of 47 years with a superficial spreading melanoma of the trunk. Her father had colon cancer at the age of 43 years and melanoma at 63 years, her uncle suffered from gastric cancer, and her grandfather had laryngeal cancer. (C) 2001 Lippincott Williams & Wilkins
Training Generative Adversarial Networks via Stochastic Nash Games
No full textGenerative adversarial networks (GANs) are a class of generative models with two antagonistic neural networks: a generator and a discriminator. These two neural networks compete against each other through an adversarial process that can be modeled as a stochastic Nash equilibrium problem. Since the associated training process is challenging, it is fundamental to design reliable algorithms to compute an equilibrium. In this article, we propose a stochastic relaxed forward-backward (SRFB) algorithm for GANs, and we show convergence to an exact solution when an increasing number of data is available. We also show convergence of an averaged variant of the SRFB algorithm to a neighborhood of the solution when only a few samples are available. In both cases, convergence is guaranteed when the pseudogradient mapping of the game is monotone. This assumption is among the weakest known in the literature. Moreover, we apply our algorithm to the image generation problem.</p
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