Let A be amatrix, c be any linear objective function and x be a fractional vector, say an LP solution to some discrete optimization problem. Then a recurring task in theoretical computer science (and in approximation algorithms in particular) is to obtain an integral vector y such that Ax ≈ Ay and c y exceeds c x by only a moderate factor. We give a new randomized rounding procedure for this ta...

Vasili Baranau1 1. Department of Chemistry, Philipps-Universität Marburg, Hans-Meerwein-Strasse, 35032 Marburg, Germany Corresponding author: Vasili Baranau, [email protected] Abstract: We propose a universal approach in the framework of the lattice Boltzmann method (LBM) to modeling constant velocity constraints and constant temperature constraints on curved walls, which doesn’t depend ...

The cross-entropy (CE) method is a new generic approach to combinatorial and multi-extremal optimization and rare event simulation. The purpose of this tutorial is to give a gentle introduction to the CE method. We present the CE methodology, the basic algorithm and its modifications, and discuss applications in combinatorial optimization and machine learning.

The cross-entropy (CE) method is an adaptive importance sampling procedure that has been successfully applied to a diverse range of complicated simulation problems. However, recent research has shown that in some high-dimensional settings, the likelihood ratio degeneracy problem becomes severe and the importance sampling estimator obtained from the CE algorithm becomes unreliable. We consider a...

Based on the algorithmic proof of Lovász local lemma due to Moser and Tardos, Esperet and Parreau developed a framework to prove upper bounds for several chromatic numbers (in particular acyclic chromatic index, star chromatic number and Thue chromatic number) using the so-called entropy compression method. Inspired by this work, we propose a more general framework and a better analysis. This l...

Reinforcement Learning methods have been succesfully applied to various optimalization problems. Scaling this up to real world sized problems has however been more of a problem. In this research we apply Reinforcement Learning to the game of Tetris which has a very large state space. We not only try to learn policies for Standard Tetris but try to learn parameterized policies for Generalized Te...

We consider a differential method of maximum entropy that is based on the linearity of Fourier transform and involves reconstruction of images from the differences of the visibility function. The efficiency of the method is demonstrated with respect to the recovery of source images with bright components against the background of a sufficiently weak extended base. The simulation results are giv...

We propose a spectral method that discretizes the Boltzmann collision operator and satisfies a discrete version of the H-theorem. The method is obtained by modifying the existing Fourier spectral method to match a classical form of the discrete velocity method. It preserves the positivity of the solution on the Fourier collocation points and as a result satisfies the H-theorem. The fast algorit...

Conformational entropy calculation, usually computed by normal mode analysis (NMA), is a time-consuming step in MM-PB/GBSA calculations. Here, instead of NMA, a solvent accessible surface area (SAS) based model was employed to compute the conformational entropy. A new fast GPU-based method called MURCIA (Molecular Unburied Rapid Calculation of Individual Areas) was used instead of the tradition...

A new class of high-order numerical methods for approximating nonlinear conservation laws is described (entropy viscosity method). The novelty is that a nonlinear viscosity based on the local size of an entropy production is added to the numerical discretization at hand. This new approach does not use any flux or slope limiters, applies to equations or systems supplemented with one or more entr...