T. C. Hu – författare

Mathematical Programming provides information pertinent to the developments in mathematical programming. This book covers a variety of topics, including integer programming, dynamic programming, game theory, nonlinear programming, and combinatorial equivalence. Organized into nine chapters, this book begins with an overview of optimization of very large-scale planning problems that can be achieved on significant problems. This text then introduces non-stationary policies and determines certain operating characteristics of the optimal policy for a very long planning horizon. Other chapters consider the perfect graph theorem by defining some well-known integer-valued functions of an arbitrary graph. This book discusses as well integer programming that deals with the class of mathematical programming problems in which some or all of the variables are required to be integers. The final chapter deals with the basic theorem of game theory. This book is a valuable resource for readers who are interested in mathematical programming. Mathematicians will also find this book useful.

This textbook provides concise coverage of the basics of linear and integer programming which, with megatrends toward optimization, machine learning, big data, etc., are becoming fundamental toolkits for data and information science and technology. The authors’ approach is accessible to students from almost all fields of engineering, including operations research, statistics, machine learning, control system design, scheduling, formal verification and computer vision. The presentations enables the basis for numerous approaches to solving hard combinatorial optimization problems through randomization and approximation.  Readers will learn to cast various problems that may arise in their research as optimization problems, understand the cases where the optimization problem will be linear, choose appropriate solution methods and interpret results appropriately.

This textbook provides concise coverage of the basics of linear and integer programming which, with megatrends toward optimization, machine learning, big data, etc., are becoming fundamental toolkits for data and information science and technology. The authors’ approach is accessible to students from almost all fields of engineering, including operations research, statistics, machine learning, control system design, scheduling, formal verification and computer vision. The presentations enables the basis for numerous approaches to solving hard combinatorial optimization problems through randomization and approximation.  Readers will learn to cast various problems that may arise in their research as optimization problems, understand the cases where the optimization problem will be linear, choose appropriate solution methods and interpret results appropriately.