Linear Programming Books - Page 12

The Deductive Foundations of Computer Programming

Zohar Manna, Richard Waldinger

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Customer Reviews:
Total reviews: 2 Average rating: 5.0 of 5

Excellent Textbook for a First Logic Course in CS 5 out of 5 stars.
6 of 6 people found this review helpful.

I really liked this book. It is full of examples, and the explanations are clear and concise. The book guides you through predicate logic and first order logic and procedures for automated deductive systems for them. No previous knowledge is assumed. The book is also full of exercises and problems.

I do recommend this book for students of Computer Science and Mathematics who want to start learning about automated deductive systems and axiomatic theories.

A Good Start Toward Automated Reasoning 5 out of 5 stars.
6 of 6 people found this review helpful.

This book is a good introductory catalog of automated reasoning concepts and methods. But I believe that programmers who are going to write automated reasoning systems should augment their computer knowledge (of logic) by learning a full- blown (serial, not tree) logical notation for both 1st and 2nd order predicate calculus.

Solving the inference problem is NP-Complete, and computational cycles explode as the number of premisses increase. Automated methods of solving the inference problem are often O(n^2) and often infeasible, for "real-world" problems. Most programmers don't have a broad enough horizon in formal logic to quickly recognize WHEN a certain automated technique will be useful.

I suggest a symbolic logic text such as Copi's Symbolic Logic, 5th ed., and then aggressively read about optimizing methods which are currently being developed (ex. Optimization Methods for Logical Inference, Chandru et alia).

However, as an introduction, the reviewed book is excellent.

Large-Scale Nonlinear Optimization (Nonconvex Optimization and Its Applications)

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Editorial Review:

Large-Scale Nonlinear Optimization reviews and discusses recent advances in the development of methods and algorithms for nonlinear optimization and its applications, focusing on the large-dimensional case, the current forefront of much research.

The chapters of the book, authored by some of the most active and well-known researchers in nonlinear optimization, give an updated overview of the field from different and complementary standpoints, including theoretical analysis, algorithmic development, implementation issues and applications.

Introduction to Applied Optimization (Springer Optimization and Its Applications)

Urmila Diwekar

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Editorial Review:

The wide scope of optimization mandates extensive interaction between various disciplines in the development of the methods and algorithms, and in their fruitful application to real-world problems.

This book presents a discipline-independent view of optimization, providing opportunities for students to identify and apply algorithms, methods, and tools from the diverse areas of optimization to their own fields without getting into too much detail about the underlying theories. The second edition of this book includes two new chapters: a chapter on global optimization and a real-world case study that uses principles from each chapter.

Key Features: (1) Provides self-contained chapters, including problem sets and exercises; (2) Introduces applied optimization with several unique applications, i.e., hazardous waste blending problem; (3) Explores a number or important methods, i.e., the simplex method, weighting method, constraint method, and goal programming method; (4) Explores several different types of optimization, i.e., discrete, global, multi-objective, and dynamic optimization; (5) Includes an extensive bibliography at the end of each chapter.

This book is intended for a variety of scientists, engineers, researchers, and advanced students interested in applied optimization.

Linear and Nonlinear Programming

Stephen G. Nash, Ariela Sofer

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Customer Reviews:
Total reviews: 4 Average rating: 3.0 of 5

clear 4 out of 5 stars.
7 of 9 people found this review helpful.

In my opininon it is more clear than the other examples of this category. But a deficiency for this book is there is no solutions to the problems as exercises.

There are better books... 4 out of 5 stars.
2 of 3 people found this review helpful.

I found this book to be not so good for non linear programming. The notation is confusing and the discussion of subject itself is not organised. I prefer other books like the book by Bertsekas. I followed Bazaara's book for linear programming and hence I dont know about this book's linear programming.

nothing special 3 out of 5 stars.
2 of 3 people found this review helpful.

the fact that none of the exercises have solutions is bothersome. i also had difficulty determining which ideas/equations have more emphasis than others. this book shouldn't be used as an introductory course due to its mediocre-at-best layout.

Bad presentation 1 out of 5 stars.
0 of 0 people found this review helpful.

This book explains linear programming concepts using tableaus. Tableaus are obsolete. If you want to solve a linear program, you use Maple, Mathematica, Matlab, GNU Octave or some other computer program. If you want to understand the simplex algorithm, you should study the linear algebra approach, and not tableaus.

The book is badly organized, the font is ugly and the theory is spread over zillions of examples. Definitions are hard find. The algorithms are presented in a very cumbersome manner. The exercises are so many you don't bother even to read them. I would be nicer with a few, carefully selected, exercises.

The book would not be nice for an introductory course since everything is presented in such an obfusacted way. For a second course, it's not useful either, since instead of solid theory it contains only the mentioned zillions of examples.

Editorial Review:

This text presents linear and nonlinear programming in an integrated setting and serves as a complete and unified introduction to applications, theory, and algorithms.

Introduction to Optimal Control Theory (Undergraduate Texts in Mathematics)

Jack Macki, Aaron Strauss

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Editorial Review:

This is an introduction to optimal control theory for systems governed by vector ordinary differential equations, up to and including a proof of the Pontryagin Maximum Principle. Though the subject is accessible to any student with a sound undergraduate mathematics background. Theory and applications are integrated with examples, particularly one special example (the rocket car) which relates all the abstract ideas to an understandable setting. The authors avoid excessive generalization, focusing rather on motivation and clear, fluid explanation.

Linear Programming: Methods and Applications

Saul I. Gass

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Customer Reviews:
Total reviews: 2 Average rating: 4.5 of 5

Linear Programming: Methods and Applications: Fifth Edition 5 out of 5 stars.
10 of 10 people found this review helpful.

This book is excellent for everyone who is studying Linear Programming. What is especially useful is the large number of references that are included at the end of each chapter. The second thing is the inclusion of introductory material in chapters 1 to 4 for the first time student. The third thing that is good is the inclusion of many applications, making the book very useful for the experienced student of Operations Reserch. The fourth is the abundance of exercises at the end of each chapter. I strongly recommend this book for students, especially those who are from disciplines outside Mathematics, who will use computational techniques of Linear Programming.

Editorial Review:

Clear and comprehensive, this volume introduces theoretical, computational, and applied concepts and is useful both as text and as a reference book. Considerations of theoretical and computational methods include the general linear programming problem, the simplex computational procedure, the revised simplex method, more. Examples and exercises with selected answers appear in every chapter. 1995 edition.

Stochastic Linear Programming: Models, Theory, and Computation (International Series in Operations Research & Management Science)

Peter Kall, Janos Mayer

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Editorial Review:

Peter Kall and János Mayer are distinguished scholars and professors of Operations Research and their research interest is particularly devoted to the area of stochastic optimization. Stochastic Linear Programming: Models, Theory, and Computation is a definitive presentation and discussion of the theoretical properties of the models, the conceptual algorithmic approaches, and the computational issues relating to the implementation of these methods to solve problems that are stochastic in nature. The application area of stochastic programming includes portfolio analysis, financial optimization, energy problems, random yields in manufacturing, risk analysis, etc. In this book, models in financial optimization and risk analysis are discussed as examples, including solution methods and their implementation.

Stochastic programming is a fast developing area of optimization and mathematical programming. Numerous papers and conference volumes, and several monographs have been published in the area; however, the Kall and Mayer book will be particularly useful in presenting solution methods including their solid theoretical basis and their computational issues, based in many cases on implementations by the authors. The book is also suitable for advanced courses in stochastic optimization.

Introduction to Linear Programming (Pure and Applied Mathematics)

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Customer Reviews:
Total reviews: 1 Average rating: 5.0 of 5

Way to go Dr. D! 5 out of 5 stars.
0 of 4 people found this review helpful.

This was one of the best books I've read. Even if you are just a high school principal, you are a tremendous writer and I hope you continue to strike fear into the hearts of readers everywhere! You rock Triple D!

Editorial Review:

Stressing the use of several software packages based on simplex method variations, this text teaches linear programming's four phases through actual practice. It shows how to decide whether LP models should be applied, set up appropriate models, use software to solve them, and examine solutions to a

Introduction to Linear Programming

Leonid N. Vaserstein

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Total reviews: 1 Average rating: 5.0 of 5

Great book for learning on your own 5 out of 5 stars.
1 of 1 people found this review helpful.

I was actually a student in Professor Byrne's class at Penn State University when he and Professor Vaserstein were working on the book. The bulk of the course material and layout of that course eventually became the core of this book. This is good because the course was excellent. With no experience in LP (linear programming) you easily move through this book from cover to cover. Both graphical and SIMPLEX methods are thoroughly discussed. The examples are very good and they demonstrate real world applications.

Editorial Review:

This book makes Linear Programming easily accessible to any reader, regardless of his or her mathematical background. The emphasis on logic and modeling of applications enables those will little mathematical background to learn to use Linear Programming in their respective fields (business, economics, operations research, etc.). This book is useful for anyone interested in learning real-world applications of mathematics or for computer scientists who want to test their programming skills.

Theory of Linear and Integer Programming

Alexander Schrijver

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Editorial Review:

Theory of Linear and Integer Programming Alexander Schrijver Centrum voor Wiskunde en Informatica, Amsterdam, The Netherlands This book describes the theory of linear and integer programming and surveys the algorithms for linear and integer programming problems, focusing on complexity analysis. It aims at complementing the more practically oriented books in this field. A special feature is the author's coverage of important recent developments in linear and integer programming. Applications to combinatorial optimization are given, and the author also includes extensive historical surveys and bibliographies. The book is intended for graduate students and researchers in operations research, mathematics and computer science. It will also be of interest to mathematical historians. Contents 1 Introduction and preliminaries; 2 Problems, algorithms, and complexity; 3 Linear algebra and complexity; 4 Theory of lattices and linear diophantine equations; 5 Algorithms for linear diophantine equations; 6 Diophantine approximation and basis reduction; 7 Fundamental concepts and results on polyhedra, linear inequalities, and linear programming; 8 The structure of polyhedra; 9 Polarity, and blocking and anti-blocking polyhedra; 10 Sizes and the theoretical complexity of linear inequalities and linear programming; 11 The simplex method; 12 Primal-dual, elimination, and relaxation methods; 13 Khachiyan's method for linear programming; 14 The ellipsoid method for polyhedra more generally; 15 Further polynomiality results in linear programming; 16 Introduction to integer linear programming; 17 Estimates in integer linear programming; 18 The complexity of integer linear programming; 19 Totally unimodular matrices: fundamental properties and examples; 20 Recognizing total unimodularity; 21 Further theory related to total unimodularity; 22 Integral polyhedra and total dual integrality; 23 Cutting planes; 24 Further methods in integer linear programming; Historical and further notes on integer linear programming; References; Notation index; Author index; Subject index