The emergence of high-performance computers and sophisticated software tech nology has led to significant advances in the development and application of operations research. In turn, the growing complexity of operations research models has posed an increasing challenge to computational methodology and computer technology. This volume focuses on recent advances in the fields of Computer Science and Operations Research, on the impact of technologi cal innovation on these disciplines, and on the close interaction between them. The papers cover many relevant topics: computational probability; design and analysis of algorithms; graphics; heuristic search and learning; knowledge-based systems; large-scale optimization; logic modeling and computation; modeling languages; parallel computation; simulation; and telecommunications. 1 This volume developed out of a conference held in Williamsburg, Virginia, January 5-7, 1994. It was sponsored by the Computer Science Technical Section of the Operations Research Society of America. The conference was attended by over 120 people from across the United States, and from many other countries. We would like to take this opportunity to thank the participants of the con ference, the authors, the anonymous referees, and the publisher for helping produce this volume. We express our special thanks to Bill Stewart and Ed Wasil for serving as Area Editors."

The emergence of high-performance computers and sophisticated software tech nology has led to significant advances in the development and application of operations research. In turn, the growing complexity of operations research models has posed an increasing challenge to computational methodology and computer technology. This volume focuses on recent advances in the fields of Computer Science and Operations Research, on the impact of technologi cal innovation on these disciplines, and on the close interaction between them. The papers cover many relevant topics: computational probability; design and analysis of algorithms; graphics; heuristic search and learning; knowledge-based systems; large-scale optimization; logic modeling and computation; modeling languages; parallel computation; simulation; and telecommunications. 1 This volume developed out of a conference held in Williamsburg, Virginia, January 5-7, 1994. It was sponsored by the Computer Science Technical Section of the Operations Research Society of America. The conference was attended by over 120 people from across the United States, and from many other countries. We would like to take this opportunity to thank the participants of the con ference, the authors, the anonymous referees, and the publisher for helping produce this volume. We express our special thanks to Bill Stewart and Ed Wasil for serving as Area Editors.

Introduces the applications, theory, and algorithms of linear and nonlinear optimization, with an emphasis on the practical aspects of the material. Its unique modular structure provides flexibility to accommodate the varying needs of instructors, students, and practitioners with different levels of sophistication in these topics. The succinct style of this second edition is punctuated with numerous real-life examples and exercises, and the authors include accessible explanations of topics that are not often mentioned in textbooks, such as duality in nonlinear optimization, primal-dual methods for nonlinear optimization, filter methods, and applications such as support-vector machines. Part I provides fundamentals that can be taught in whole or in part at the beginning of a course on either topic and then referred to as needed. Part II on linear programming and Part III on unconstrained optimization can be used together or separately, and Part IV on nonlinear optimization can be taught without having studied the material in Part II. In the preface the authors suggest course outlines that can be adjusted to the requirements of a particular course on both linear and nonlinear optimization, or to separate courses on these topics. Three appendices provide information on linear algebra, other fundamentals, and software packages for optimization problems. A supplemental website offers auxiliary data sets that are necessary for some of the exercises.