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* Written by a recognized authority in the area of optimization software, this text offers an array of information on the latest advances in optimization techniques, explaining both theory and practice * Specializes in non-linear programming, mixed-integer programming, and global optimization * Ample references explore theoretical concepts in more detail

The investigation of phenomena involving fractals has gone through a spectacular development in the last decade. Many physical, technological and biological processes have been shown to be related to and described by objects with non-integer dimensions. The physics of far-from-equilibrium growth phenomena represents one of the most important fields in which fractal geometry is widely applied. During the last couple of years considerable experimental, numerical and theoretical information has accumulated concerning such processes.This book, written by a well-known expert in the field, summarizes the basic concepts born in the studies of fractal growth and also presents some of the most important new results for more specialized readers. It also contains 15 beautiful color plates demonstrating the richness of the geometry of fractal patterns. Accordingly, it may serve as a textbook on the geometrical aspects of fractal growth and it treats this area in sufficient depth to make it useful as a reference book. No specific mathematical knowledge is required for reading this book which is intended to give a balanced account of the field.

This book offers a compendium of problems in operations research, in such areas as linear programming, integer programming, non-linear programming, network modeling, inventory theory, queue theory, tree decision, dynamic programming, Markov processes and more.

* Written by a recognized authority in the area of optimization software, this text offers an array of information on the latest advances in optimization techniques, explaining both theory and practice * Specializes in non-linear programming, mixed-integer programming, and global optimization * Ample references explore theoretical concepts in more detail

This book offers a compendium of problems in operations research, in such areas as linear programming, integer programming, non-linear programming, network modeling, inventory theory, queue theory, tree decision, dynamic programming, Markov processes and more.

Discover the art and science of solving artificial intelligence problems with Python using optimization modeling. This book covers the practical creation and analysis of mathematical algebraic models such as linear continuous models, non-obviously linear continuous models,and pure linear integer models. Rather than focus on theory, Practical Python AI Projects, the product of the author's decades of industry teaching and consulting, stresses the model creation aspect; contrasting alternate approaches and practical variations.Each model is explained thoroughly and written to be executed. The source code from all examples in the book is available, written in Python using Google OR-Tools. It also includes a random problem generator, useful for industry application or study.What You Will LearnBuild basic Python-based artificial intelligence (AI) applications Work with mathematical optimization methods and the Google OR-Tools (Optimization Tools) suiteCreate several types of projects using Python and Google OR-ToolsWho This Book Is ForDevelopers and students who already have prior experience in Python coding. Some prior mathematical experience or comfort level may be helpful as well.  

An exceptionally comprehensive treatment of this subject aimed at students in business, management, science, and engineering. Topics include linear, non-linear, integer, and dynamic programming, network analysis, quadratic and separable programming, inventory control, probabilistic methods, and many other topics. Numerous applications.

The investigation of phenomena involving fractals has gone through a spectacular development in the last decade. Many physical, technological and biological processes have been shown to be related to and described by objects with non-integer dimensions. The physics of far-from-equilibrium growth phenomena represents one of the most important fields in which fractal geometry is widely applied. During the last couple of years considerable experimental, numerical and theoretical information has accumulated concerning such processes.This book, written by a well-known expert in the field, summarizes the basic concepts born in the studies of fractal growth and also presents some of the most important new results for more specialized readers. It also contains 15 beautiful color plates demonstrating the richness of the geometry of fractal patterns. Accordingly, it may serve as a textbook on the geometrical aspects of fractal growth and it treats this area in sufficient depth to make it useful as a reference book. No specific mathematical knowledge is required for reading this book which is intended to give a balanced account of the field.

Discover the art and science of solving artificial intelligence problems with Python using optimization modeling. This book covers the practical creation and analysis of mathematical algebraic models such as linear continuous models, non-obviously linear continuous models,and pure linear integer models. Rather than focus on theory, Practical Python AI Projects, the product of the author's decades of industry teaching and consulting, stresses the model creation aspect; contrasting alternate approaches and practical variations.Each model is explained thoroughly and written to be executed. The source code from all examples in the book is available, written in Python using Google OR-Tools. It also includes a random problem generator, useful for industry application or study.What You Will LearnBuild basic Python-based artificial intelligence (AI) applications Work with mathematical optimization methods and the Google OR-Tools (Optimization Tools) suiteCreate several types of projects using Python and Google OR-ToolsWho This Book Is ForDevelopers and students who already have prior experience in Python coding. Some prior mathematical experience or comfort level may be helpful as well.