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

Graphs, Algorithms, and Optimization

ISBN: 1584883960,9781584883968 | 305 pages | 8 Mb

Graphs, Algorithms, and Optimization Donald L. Kreher, William Kocay
Publisher: Chapman and Hall/CRC

Given the OBDD as an input, symbolic/implicit OBDD-based graph algorithms can solve optimization problems by mainly using functional operations, e.g. Social Influence – Analyze and score social graphs to identify top influencers and high-value user types. Considering the communication costs among the processors, two efficient mapping algorithms are proposed. Dynamic Optimization – Content optimization on websites to increase customer conversion. This mapping problem is formulated as an equivalent problem of graph partitioning and modules allocation problem. We've used MATLAB for the same. An example of each would be: Predictive Analytics – predict customer churn. This project deals with different Optimization and Graph algorithms and creating a user friendly GUI utility for users. Optimization/Graph GUI Utility in MATLAB. Quantification or binary synthesis. Andy- Right now, we think about our algorithms as addressing three types of business needs: predictive analytics, dynamic optimization, and social influence. There was a high-profile report that I saw quoted this year with a graph which claimed that large-scale magnetohydrodynamics problem speed improvements are evenly distributed between software and hardware:. And the algorithm optimization I am aware of tries to minimize the number of cycles that a single process requires, rather than tradeoffs between the total number of cycles required for a task and the number of operations dependent on the results of other .

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