リサン クミアワセ モンダイ ノ セイヤク ジュウソク モンダイ ニ ヨル テイシキカ ナラビニ セイゴウカ シュホウ ノ テキヨウ ケントウ

本論文では、離散組み合わせ問題、特に、地図の彩色問題とクロスワードパズル問題を制約充足問題として定式化するとともに、整合化手法を用いた制約充足解法を適用し、その有効性を示す。In this paper, we describe a formalization of discrete combinatorial problems from viewpoint of constraint satisfaction problems and the consistency methods for constraint satisfaction problems. Effectiveness of these consistency methods is shown using application of these methods to map coloring problem and crossword puzzle problem

Choosing Colors for Geometric Graphs via Color Space Embeddings

Graph drawing research traditionally focuses on producing geometric embeddings of graphs satisfying various aesthetic constraints. After the geometric embedding is specified, there is an additional step that is often overlooked or ignored: assigning display colors to the graph's vertices. We study the additional aesthetic criterion of assigning distinct colors to vertices of a geometric graph so that the colors assigned to adjacent vertices are as different from one another as possible. We formulate this as a problem involving perceptual metrics in color space and we develop algorithms for solving this problem by embedding the graph in color space. We also present an application of this work to a distributed load-balancing visualization problem.Comment: 12 pages, 4 figures. To appear at 14th Int. Symp. Graph Drawing, 200

An extensive English language bibliography on graph theory and its applications, supplement 1

Graph theory and its applications - bibliography, supplement

On the use of associative memory in Hopfield networks designed to solve propositional satisfiability problems

Hopfield networks are an attractive choice for solving many types of computational problems because they provide a biologically plausible mechanism. The Self-Optimization (SO) model adds to the Hopfield network by using a biologically founded Hebbian learning rule, in combination with repeated network resets to arbitrary initial states, for optimizing its own behavior towards some desirable goal state encoded in the network. In order to better understand that process, we demonstrate first that the SO model can solve concrete combinatorial problems in SAT form, using two examples of the Liars problem and the map coloring problem. In addition, we show how under some conditions critical information might get lost forever with the learned network producing seemingly optimal solutions that are in fact inappropriate for the problem it was tasked to solve. What appears to be an undesirable side-effect of the SO model, can provide insight into its process for solving intractable problems.Comment: 7 pages, 4 figure

The Hyperion system: Compiling multithreaded Java bytecode for distributed execution

A preliminary version of this work has been presented as a Distinguished Paper at the Euro-Par 2000 Conference, Munich, Germany, August 2000.International audienceOur work combines Java compilation to native code with a runtime library that executes Java threads in a distributed memory environment. This allows a Java programmer to view a cluster of processors as executing a single JAVA virtual machine. The separate processors are simply resources for executing Java threads with true parallelism, and the run-time system provides the illusion of a shared memory on top of the private memories of the processors. The environment we present is available on top of several UNIX systems and can use a large variety of communication interfaces thanks to the high portability of its run time system. To evaluate our approach, we compare serial C, serial Java, and multithreaded Java implementations of a branch and-bound solution to the minimal-cost map-coloring problem. All measurements have been carried out on two platforms using two different communication interfaces: SISCI/SCI and MPI BIP/Myrinet