no code implementations • 13 Oct 2023 • Alexandros Hollender, Manolis Zampetakis

Finding approximate stationary points, i. e., points where the gradient is approximately zero, of non-convex but smooth objective functions $f$ over unrestricted $d$-dimensional domains is one of the most fundamental problems in classical non-convex optimization.

no code implementations • 4 Mar 2021 • Aris Filos-Ratsikas, Yiannis Giannakopoulos, Alexandros Hollender, Philip Lazos, Diogo Poças

We consider the problem of computing a (pure) Bayes-Nash equilibrium in the first-price auction with continuous value distributions and discrete bidding space.

Computer Science and Game Theory Computational Complexity

1 code implementation • 3 Nov 2020 • John Fearnley, Paul W. Goldberg, Alexandros Hollender, Rahul Savani

We study search problems that can be solved by performing Gradient Descent on a bounded convex polytopal domain and show that this class is equal to the intersection of two well-known classes: PPAD and PLS.

no code implementations • NeurIPS 2020 • Georgios Birmpas, Jiarui Gan, Alexandros Hollender, Francisco J. Marmolejo-Cossío, Ninad Rajgopal, Alexandros A. Voudouris

For this strategic behavior to be successful, the main challenge faced by the follower is to pinpoint the payoffs that would make the learning algorithm compute a commitment so that best responding to it maximizes the follower's utility, according to his true payoffs.

2 code implementations • 13 Aug 2015 • Axel Bacher, Olivier Bodini, Alexandros Hollender, Jérémie Lumbroso

We also show how it is possible to further reduce the number of random bits consumed, by introducing a second algorithm BalancedShuffle, a variant of the Rao-Sandelius algorithm which is more conservative in the way it recursively partitions arrays to be shuffled.

Data Structures and Algorithms Discrete Mathematics

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