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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
Publisher: Chapman and Hall/CRC
ISBN: 1584883960, 9781584883968

Dynamic Optimization – Content optimization on websites to increase customer conversion. Easy to program and relatively inexpensive. Social Influence – Analyze and score social graphs to identify top influencers and high-value user types. Several optimization problems become simpler in bipartite graphs. Lessons learned: Graph algorithms require a lot of joins. For example, in search Google also uses variable-byte coding to encode part of its indexes a long time ago and has switched to other compression methods lately (In my opinion, their new method is a variation of PForDelta which is also implemented in Kamikaze and optimized in Kamikaze version 3.0.0). An example of each would be: Predictive Analytics – predict customer churn. Search indexes, graph algorithms and certain sparse matrix representations tend to make heavy use of sorted integer arrays. However by doing so we were able to derive linear time algorithm while the 'structural' Interior Point Methods (which use the form of the function to be optimized by deriving an appropriate self-concordant barrier) are not linear time. Join performance was not that good so the performance was not that good. For instance the dictionary elements could be vector of incidence of spanning trees in some fixed graph, and then the linear optimization problem can be solved with a greedy algorithm. Please refer to “Algorithms and Software for Partitioning Graphs” for more details. Andy- Right now, we think about our algorithms as addressing three types of business needs: predictive analytics, dynamic optimization, and social influence.