• S. J. Wright Optimization for machine learning.pdf

    The yearly Neural Information Processing Systems (NIPS) workshops bring together scientists with broadly varying backgrounds in statistics, mathe- matics, computer science, physics, electrical engineering, neuroscience, and cognitive science, unified by a common desire to develop novel computa- tional and statistical strategies for information processing and to under- stand the mechanisms for information processing in the brain. In contrast to conferences, these workshops maintain a flexible format that both allows and encourages the presentation and discussion of work in progress. They thus serve as an incubator for the development of important new ideas in this rapidly evolving field. The series editors, in consultation with work- shop organizers and members of the NIPS Foundation Board, select specific workshop topics on the basis of scientific excellence, intellectual breadth, and technical impact. Collections of papers chosen and edited by the or- ganizers of specific workshops are built around pedagogical introductory chapters, while research monographs provide comprehensive descriptions of workshop-related topics, to create a series of books that provides a timely, authoritative account of the latest developments in the exciting field of neu- ral computation.

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  • S. J. Wright Numerical Optimization(second edition).pdf

    This is a book for people interested in solving optimization problems. Because of the wide (and growing) use of optimization in science, engineering, economics, and industry, it is essential for students and practitioners alike to develop an understanding of optimization algorithms. Knowledge of the capabilities and limitations of these algorithms leads to a better understanding of their impact on various applications, and points the way to future research on improving and extending optimization algorithms and software. Our goal in this book is to give a comprehensive description of the most powerful, state-of-the-art, techniques for solving continuous optimization problems. By presenting the motivating ideas for each algorithm, we try to stimulate the reader’s intuition and make the technical details easier to follow. Formal mathematical requirements are kept to a minimum.

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    2017-10-19
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