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Graphs, Algorithms, and Optimization by Donald L. Kreher, William Kocay

Graphs, Algorithms, and Optimization Donald L. Kreher, William Kocay ebook
Page: 305
Format: pdf
ISBN: 1584883960, 9781584883968
Publisher: Chapman and Hall/CRC

This Demonstration shows the steps of Edmonds's famous blossom algorithm for finding the perfect matching of minimal weight in a complete weighted graph. Yet the approximability of several fundamental problems such as TSP, Graph Coloring, Graph Partitioning etc. Many of the striking advances in theoretical computer science over the past two decades concern approximation algorithms, which compute provably near-optimal solutions to NP-hard optimization problems. Assembled by a team of researchers from academia, industry, and national labs, the Graph 500 benchmark targets concurrent search, optimization (single source shortest path), and edge-oriented (maximal independent set) tasks. Many of the computations carried out by the algorithms are optimized by storing information that reflects the results of past computations. Most graph databases (such as GraphLab uses similar primitives (called PowerGraph) but allows for asynchronous iterative computations, leading to an expanded set of (potentially) faster algorithms. [3] Egerváry Research Group on Combinatorial Optimization. Research Areas: Computational Complexity, Graph Theory and Combinatorial Optimization. A traversal is an algorithmic/directed walk over the graph such that paths are determined (called derivations) or information is gleaned (called statistics). Matching algorithms pull data from various databases to “flesh out” search results with advertising, local data, knowledge graph data, image data, video data, news data, etc. Prerequisites: Reasonable mathematical maturity, knowledge of algorithm design and analysis.