Noticias en español
Generalized Assignment and Knapsack Problems in the Random-Order Model
Abstract: We study different online optimization problems in the random-order model. There is a finite set of bins with known capacity and a finite set of items arriving in a random order. Upon arrival of an item, its size and its value for each of the bins is revealed and it has to be decided immediately and irrevocably to which bin the item is assigned, or to not assign the item at all. In this setting, an algorithm is $\alpha$-competitive if the total value of all items assigned to the bins is at least an $\alpha$-fraction of the total value of an optimal assignment that knows all...
Read MoreExpansion of random 0/1 polytopes and the Mihail and Vazirani conjecture.
Abstract: A 0/1 polytope is the convex hull of a set of 0/1 d-dimensional vectors. A conjecture of Milena Mihail and Umesh Vazirani says that the graph of vertices and edges of every 0/1 polytope is highly connected. Specifically, it states that the edge expansion of the graph of every 0/1 polytope is at least one. Any lower bound on the edge expansion gives an upper bound for the mixing time of a random walk on the graph of the polytope. Such random walks are important because they can be used to generate an element from a set of combinatorial objects uniformly at random. A weaker form of...
Read MoreA Normality Conjecture on Rational Base Number Systems
RESUMEN: The rational base number system, introduced by Akiyama, Frougny, and Sakarovitch in 2008, is a generalization of the classical integer base number system. Within this framework two interesting families of infinite words emerge, called minimal and maximal words. We formulate the conjecture that every minimal and maximal word is normal over an appropriate subalphabet. The aim of the talk is to convince the audience that the conjecture seems true and of considerable difficulty. In particular, we shall discuss its connections with several older conjectures, including the existence of...
Read MoreThermodynamics-informed Neural Networks (THINNs).
Abstract: Physics-Informed Neural Networks (PINNs) are a class of deep learning models aiming to approximate solutions of PDEs by training neural networks to minimize the residual of the equation. Focusing on non-equilibrium fluctuating systems, we propose a physically informed choice of penalization that is consistent with the underlying fluctuation structure, as characterized by a large deviations principle. This approach yields a novel formulation of PINNs in which the penalty term is chosen to penalize improbable deviations, rather than being selected heuristically. The resulting...
Read MoreChen-Chvátal Conjecture in Graphs and Hypergraphs.
Abstract: It is well known that a set of n non-collinear points in the Euclidean plane determines at least n distinct lines. In 2008, Chen and Chvátal conjectured that this result extends to arbitrary finite metric spaces with an appropriate definition of line. In this talk, we present a survey of this conjecture, outlining known results in the contexts of metric spaces, hypergraphs, and graphs
Read MoreIdentifying the deviator.
Abstract: Alice and Bob control a random walk: alternately, each of them flips a fair coin, is supposed to report the outcome, and the random work advances according to the report. Suppose that the random walk did not return to the origin infinitely often. We suspect that one of Alice and Bob misreported the outcomes of her or his coin. Can we identify the deviator? More generally, several players are supposed to follow a prescribed profile of strategies (e,g, select each of Right and Left with probability 1/2). If they follow this profile, they will reach a given target (e.g., the...
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