In the intervening years since this book was published in 1981, the field of optimization has been exceptionally lively. This fertility has involved not only progress in theory but also faster numerical algorithms and extensions into unexpected or previously unknown areas such as semidefinite programming. Despite these changes, many of the important principles and much of the intuition can be found in this Classics version of Practical Optimization. This book provides model algorithms and pseudocode, presents algorithms in a step-by-step format, and contains a wealth of techniques and strategies that are well suited for optimization in the twenty-first century and particularly in the now-flourishing fields of data science, big data, and machine learning.

About the Author

Philip E. Gill is Distinguished Professor of Mathematics at the University of California, San Diego (UCSD). He received his Ph.D. from Imperial College London in 1974. Before joining UCSD, he was a researcher at the National Physical Laboratory in the United Kingdom and the Systems Optimization Laboratory at Stanford. His research areas include scientific computation and numerical optimization. He was elected Fellow of the Society of Industrial and Applied Mathematics (SIAM) in 2014. Walter Murray has been a professor at Stanford University since 1979. He is the director of the Systems Optimization Laboratory and was a previous director of the SCCM program and of ICME. He received his Ph.D. from the University of London in 1969 when working at the National Physical Laboratory. He has been principal advisor to 40 doctoral students from 19 countries and five continents who have graduated from a variety of universities (Stanford, London, Oxford, Royal Institute of Technology in Stockholm, South Australia). Margaret H. Wright is Silver Professor at the Courant Institute of Mathematical Sciences, New York University. She received her Ph.D. in computer science from Stanford University. Before joining NYU, she was a researcher in the Systems Optimization Laboratory at Stanford and the Computing Science Research Center at Bell Laboratories. Her interests include numerical optimization and scientific computing. She is a member of the National Academy of Sciences, the American Academy of Arts and Sciences, and the National Academy of Engineering.

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