More advanced topics also are presented including interior point algorithms, the branch-and-bound algorithm, cutting planes, complexity, standard combinatorial optimization models, the assignment problem, minimum cost flow, and the maximum flow/minimum cut theorem.
The second part applies theory through real-world case studies. The authors discuss advanced techniques such as column generation, multiobjective optimization, dynamic optimization, machine learning (support vector machines), combinatorial optimization, approximation algorithms, and game theory.
Besides the fresh new layout and completely redesigned figures, this new edition incorporates modern examples and applications of linear optimization. The book now includes computer code in the form of models in the GNU Mathematical Programming Language (GMPL). The models and corresponding data files are available for download and can be readily solved using the provided online solver.
This new edition also contains appendices covering mathematical proofs, linear algebra, graph theory, convexity, and nonlinear optimization. All chapters contain extensive examples and exercises. This textbook is ideal for courses for advanced undergraduate and graduate students in various fields including mathematics, computer science, industrial engineering, operations research, and management science.
Gerard Sierksma, PhD, University of Groningen, The Netherlands
Yori Zwols, PhD, Google UK, London
The Goal: A Process of Ongoing Improvement by Eliyahu Goldratt and Jeff Cox describes a process by which an unprofitable manufacturing operation can be made profitable. It conveys proven factory turnaround principles through a fictional story…
PLEASE NOTE: This is key takeaways and analysis of the book and NOT the original book.
Inside this Instaread of The Goal:Overview of the bookImportant PeopleKey TakeawaysAnalysis of Key Takeaways
But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope.
Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet.
Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype.
But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data.
Each chapter will cover a different technique in aspreadsheet so you can follow along:Mathematical optimization, including non-linear programming andgenetic algorithmsClustering via k-means, spherical k-means, and graphmodularityData mining in graphs, such as outlier detectionSupervised AI through logistic regression, ensemble models, andbag-of-words modelsForecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulationMoving from spreadsheets into the R programming language
You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.
This book will help you:Become a contributor on a data science teamDeploy a structured lifecycle approach to data analytics problemsApply appropriate analytic techniques and tools to analyzing big dataLearn how to tell a compelling story with data to drive business actionPrepare for EMC Proven Professional Data Science Certification
Corresponding data sets are available at www.wiley.com/go/9781118876138.
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