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 semi definite programming. Despite these changes, many of the important principles and much of the intuition can be found in this Classics version of Practical Optimization.
- Provides model algorithms and pseudo code, useful tools for users who prefer to write their own code as well as for those who want to understand externally provided code;
- Presents algorithms in a step-by-step format, revealing the overall structure of the underlying procedures and thereby allowing a high-level perspective on the fundamental differences; 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.