Overview

Most books on algorithms are narrowly focused on a single field of application. This unique book cuts across discipline boundaries, exposing readers to the most successful algorithms from a variety of fields. Algorithm derivation is a legitimate branch of the mathematical sciences driven by hardware advances and the demands of many scientific fields. The best algorithms are undergirded by beautiful mathematics. This book enables readers to look under the hood and understand how some basic algorithms operate and how to assemble complex algorithms from simpler building blocks. In Algorithms from THE BOOK, Second Edition Julia code for experimentation accompanies most algorithms, a large number of classroom-tested exercises at the end of each chapter make it suitable as a textbook, and appendices contain background material often missing in undergraduate education as well as solutions to selected problems.

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9781611978384

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Kenneth Lange is the Rosenfeld Professor of Computational Genetics at UCLA and a faculty member in the Departments of Computational Medicine, Human Genetics, and Statistics. At various times during his career, he has held appointments at the University of New Hampshire, MIT, Harvard, the University of Michigan, the University of Helsinki, and Stanford. He is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Institute for Medical and Biomedical Engineering. He won the Snedecor Award from the Joint Statistical Societies in 1993 and gave a platform presentation at the 2015 International Congress of Mathematicians. He was elected to the US National Academy of Sciences in 2020. His previously published books include Mathematical and Statistical Methods for Genetic Analysis, Numerical Analysis for Statisticians, Applied Probability, Optimization, and MM Optimization Algorithms. His research interests include human genetics, population modeling, cancer modeling, biomedical imaging, computational statistics, optimization theory, and applied stochastic processes.

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