This book/software package uniquely integrates logic and operations research. Its broad coverage provides concepts, templates, and the tools for the task of attacking difficult problems which are repeatedly encountered in decision making. The first part deals with linear programming and the second with search techniques for combinatorially hard problems. The applications discussed include product mix problems, pattern recognition, classical and probabilistic logic, financial planning, and expert systems.
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Ken McAloon, PhD, is Broeklundian Professor of Computer Science at Brooklyn College, CUNY.
Carol Tretkoff, PhD, is Professor of Computer Science at Brooklyn College, CUNY.
An innovative book/software package that integrates logic and operations research
This volume focuses on the role of optimization and computational logic in modern decision science. It stands apart from other books on this topic with a remarkable approach that uses a computer programming language with standard syntax to integrate logic, operations research, and artificial intelligence. The text brings together concepts, templates, and tools for attacking thorny problems commonly encountered in decision making, and emphasizes the interplay between applications, theory, and code.
Featuring a crystal–clear presentation and superior organization, this book fills a growing need for course materials on logic and optimization. It serves graduate and undergraduate students in artificial intelligence, operations research, mathematics, management science, and computer science. It is also an important resource for professionals working in decision support.
Optimization and Computational Logic
∗ Merges discrete and continuous problem solving methods
∗ Features programming examples that employ the small language 2LP (Linear Programming and Logic Programming)
∗ Includes the Windows version of the 2LP software and Internet addresses for other versions
∗ Discusses many applications, including resource allocation, personnel scheduling, pattern recognition, classical and probabilistic logic, financial applications, challenging puzzles, expert systems, machine scheduling, and more
∗ Presents search paradigms such as randomized local search, genetic algorithms, constrain–and–generate, marking–and–trailing, branch–and–bound, branch–and–cut, iterative deepening, and others
∗ Provides the mathematical background for linear programming and optimization problems
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