Since Hopfield proposed neural network computing for optimization and combinatorics problems, many neural network investigators have been working on optimization problems. In this book a variety of optimization problems and combinatorics problems are presented by respective experts.A very useful reference book for those who want to solve real-world applications, this book contains applications in graph theory, mathematics, stochastic computing including the multiple relaxation, associative memory and control, resource allocation problems, system identification and dynamic control, and job-stop scheduling.
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Jun Wang is a Reader in Computer Science, University College London, and the Founding Director of MSc Web Science and Big Data Analytics. His main research interests are in the areas of information retrieval, data mining and online advertising. He was a recipient of the Beyond Search award sponsored by Microsoft Research, US, in 2007; he also received the Best Doctoral Consortium award in ACM SIGIR06 for his work on collaborative filtering, the Best Paper Prizes in ECIR09 and ECIR12 for information retrieval, and the Best Paper Prize in ADKDD14 for computational advertising. He is also one of the recipients of Yahoo! FREP award 2014. He is an Area Chair of ACM SIGIR05 and has been a Senior PC member of ACM CIKM since 2012.
Since Hopfield proposed neural-network computing for optimization and combinatorics problems, many neural-network investigators have been working on optimization problems. In this book, a variety of optimization problems and combinatorics problems are presented by respective experts. The book contains applications in graph theory, mathematics, stochastic computing including the multiple relaxation, associative memory and control, resource allocation problems, system identification and dynamic control and job-stop scheduling. It should be useful in solving real-world applications.
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