Optimization Methods, Theory and Applications

Springer
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This book presents the latest research findings and state-of-the-art solutions on optimization techniques and provides new research direction and developments. Both the theoretical and practical aspects of the book will be much beneficial to experts and students in optimization and operation research community. It selects high quality papers from The International Conference on Optimization: Techniques and Applications (ICOTA2013). The conference is an official conference series of POP (The Pacific Optimization Research Activity Group; there are over 500 active members). These state-of-the-art works in this book authored by recognized experts will make contributions to the development of optimization with its applications.
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About the author

Professor Honglei Xu' research interests include theory and applications of nonlinear control and systems engineering.

Prof. Song Wang is a professor of Department of Mathematics and Statistics, The University of Western Australia. He has published over 100 research papers in various international journals including Automatica, Biomaterials, IEEE Trans. on Neural Networks, J. Comp. Phys., Math. Comp, IMA J Numer. Anal, Int. J. Bifurcation & Chaos, Numer. Math., Reports on Progress in Physics, SIAM J Numer, Anal, SIAM J. on Optimization.

Prof. SOON-YI WU's research interests include functional analysis, linear programming, optimization.

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Additional Information

Publisher
Springer
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Published on
Jun 17, 2015
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Pages
205
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ISBN
9783662470442
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Language
English
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Genres
Business & Economics / Operations Research
Computers / Software Development & Engineering / General
Mathematics / Applied
Mathematics / General
Mathematics / Optimization
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Content Protection
This content is DRM protected.
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Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.

But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.

Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. 

Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. 

But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.

 Each chapter will cover a different technique in a spreadsheet so you can follow along:

Mathematical optimization, including non-linear programming and genetic algorithms Clustering via k-means, spherical k-means, and graph modularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, and bag-of-words models Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation Moving from spreadsheets into the R programming language

You get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

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