Hypercube

Summary

In geometry, a hypercube is an n-dimensional analogue of a square (n = 2) and a cube (n = 3). It is a closed, compact, convex figure whose 1-skeleton consists of groups of opposite parallel line segments aligned in each of the space's dimensions, perpendicular to each other and of the same length. A unit hypercube's longest diagonal in n dimensions is equal to \sqrt{n}. An n-dimensional hypercube is more commonly referred to as an n-cube or sometimes as an n-dimensional cube. The term measure polytope (originally from Elte, 1912) is also used, notably in the work of H. S. M. Coxeter who also labels the hypercubes the γn polytopes. The hypercube is the special case of a hyperrectangle (also called an n-orthotope). A unit hypercube is a hypercube whose side has length one unit. Often, the hypercube whose corners (or vertices) are the 2n points in Rn with each coordinate equal to 0 or 1 is called the unit hypercu

Official source

https://en.wikipedia.org/wiki/Hypercube

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Efficient Data Structures For Decision Diagrams

Stéphane Rabie

Dynamic Programming Optimization (DPOP) is an algorithm proposed to solve distributed constraint optimization problems. In order to represent the multi-values functions manipulated in this algorithm, a data structure called Hypercube was implemented. A more efficient data structure, the Utility Diagram, was then proposed as an alternative to the Hypercube. DPOP also required the implementation of several operations (such as join, project, slice, split and reorder) on these two data structures. This project is a follow-up of Nacereddine Ouaret’s master thesis, which consisted in implementing all of these data structures and theirs associated operations. As DPOP may have to work on very large decision diagrams, and perform a lot of successive operations on them, having implementations which are efficient in term of speed and memory is critical. The aim of this project was therefore to seek for new ways to improve the already implemented functions for hypercubes and utility diagrams, both in term of execution time and memory consumption. This report will thus first present a quick overview of hypercubes, utility diagrams, and their associated operations (a more complete description of these objects, as well as the details about their original implementation, can be found in Nacereddine Ouaret’s report on Efficient Data Structures for Decision Diagrams). The second part will then cover various improvements made to their implementation during the course of this project. Finally, a variant for the methods used by hypercube, more economical in term of memory as it reuses existing hypercubes rather than creating new ones, will be presented.

2009

Local trapping for elliptic random walks in random environments in Z(d)

Daniel Kious

We consider elliptic random walks in i.i.d. random environments on . The main goal of this paper is to study under which ellipticity conditions local trapping occurs. Our main result is to exhibit an ellipticity criterion for ballistic behavior which extends previously known results. We also show that if the annealed expected exit time of a unit hypercube is infinite then the walk has zero asymptotic velocity.

Springer Heidelberg2016

The size of the sync basin revisited

Robin Delabays, Philippe Jacquod

In dynamical systems, the full stability of fixed point solutions is determined by their basins of attraction. Characterizing the structure of these basins is, in general, a complicated task, especially in high dimensionality. Recent works have advocated to quantify the non-linear stability of fixed points of dynamical systems through the relative volumes of the associated basins of attraction [Wiley et al., Chaos 16, 015103 (2006) and Menck et al. Nat. Phys. 9, 89 (2013)]. Here, we revisit this issue and propose an efficient numerical method to estimate these volumes. The algorithm first identifies stable fixed points. Second, a set of initial conditions is considered that are randomly distributed at the surface of hypercubes centered on each fixed point. These initial conditions are dynamically evolved. The linear size of each basin of attraction is finally determined by the proportion of initial conditions which converge back to the fixed point. Armed with this algorithm, we revisit the problem considered by Wiley et al. in a seminal paper [Chaos 16, 015103 (2006)] that inspired the title of the present manuscript and consider the equal-frequency Kuramoto model on a cycle. Fixed points of this model are characterized by an integer winding number q and the number n of oscillators. We find that the basin volumes scale as (1 - 4q/n)(n), contrasting with the Gaussian behavior postulated in the study by Wiley et al.. Finally, we show the applicability of our method to complex models of coupled oscillators with different natural frequencies and on meshed networks. Published by AIP Publishing.

Amer Inst Physics2017