Topic
Adjacency list
About: Adjacency list is a research topic. Over the lifetime, 4419 publications have been published within this topic receiving 78449 citations.
Papers published on a yearly basis
Papers
More filters
05 Sep 2011
TL;DR: The incremental breadth-first search (IBFS) method is introduced, which uses ideas from BK but augments on shortest paths and usually outperforms BK on vision problems.
Abstract: Maximum flow and minimum s-t cut algorithms are used to solve several fundamental problems in computer vision. These problems have special structure, and standard techniques perform worse than the special-purpose Boykov-Kolmogorov (BK) algorithm. We introduce the incremental breadth-first search (IBFS) method, which uses ideas from BK but augments on shortest paths. IBFS is theoretically justified (runs in polynomial time) and usually outperforms BK on vision problems.
91 citations
TL;DR: Two versions of an algorithm for the computation of all nondominated extreme points in the outcome set of a multiobjective integer programme are presented and adjacency of these points based on weight space decomposition is defined.
Abstract: In this paper, we present two versions of an algorithm for the computation of all nondominated extreme points in the outcome set of a multiobjective integer programme. We define adjacency of these points based on weight space decomposition. Thus, our algorithms generalise the well-known dichotomic scheme to compute the set of nondominated extreme points in the outcome set of a biobjective programme. Both algorithms are illustrated with and numerically tested on instances of the assignment and knapsack problems with three objectives.
91 citations
TL;DR: In this paper, the spectral properties of the adjacency and the Laplacian matrices of random graphs were investigated and it was shown that the largest eigenvalues of these matrices are dense in a compact interval almost surely.
Abstract: In this paper, we investigate the spectral properties of the adjacency and the Laplacian matrices of random graphs. We prove that: (i) the law of large numbers for the spectral norms and the largest eigenvalues of the adjacency and the Laplacian matrices; (ii) under some further independent conditions, the normalized largest eigenvalues of the Laplacian matrices are dense in a compact interval almost surely; (iii) the empirical distributions of the eigenvalues of the Laplacian matrices converge weakly to the free convolution of the standard Gaussian distribution and the Wigner’s semi-circular law; (iv) the empirical distributions of the eigenvalues of the adjacency matrices converge weakly to the Wigner’s semi-circular law.
91 citations
TL;DR: In this paper, it was shown that no two non-isomorphic lollipop graphs are cospectral with respect to the adjacency matrix, and for p odd the LLLP graphs are determined by its Laplacian spectrum.
90 citations
TL;DR: The performance of the recogniser in terms of speed is far better than that of any other rule-based system due to the Neural Network approach employed and the basic limitation is that of the heuristics used to break down compound features into simple ones which are fed to the ANN.
Abstract: This work presents a Feature Recognition system developed using a previously trained Artificial Neural Network. The part description is taken from a B-rep solid modeller's data base. This description refers only to topological information about the faces in the part in the form of an Attributed Adjacency Graph. A set of heuristics is used for breaking down this compound feature graph into subgraphs, that correspond to simple features. Special representation patterns are then constructed for each of these subgraphs. These patterns are presented to a Neural Network which classifies them into feature classes: pockets, slots, passages, protrusions, steps, blind slots, corner pockets, and holes. The scope of instances/ variations of these features that can be recognised is very wide. A commercially available neural network modelling tool was used for training. The user interface to the neural network recogniser has been written in Pascal. The program can handle parts with up to 200 planar or curved faces. The performance of the recogniser in terms of speed is far better than that of any other rule-based system due to the Neural Network approach employed. The basic limitation is that of the heuristics used to break down compound features into simple ones which are fed to the ANN, but this is still a step ahead compared to other approaches.
90 citations