Conditional Gradient Methods
From Core Principles to AI Applications
Will be published approx. on 31. December 2025
Book
Paperback/Softback
273 pages
978-1-61197-875-9 (ISBN)
Description
This book is a self-contained introduction to quantum algorithms with an emphasis on quantum optimization, that is, quantum algorithms to solve optimization problems. The book provides all the tools necessary to understand the benefits and drawbacks of quantum optimization algorithms, paying particular attention to provable guarantees and computational complexity.
The first comprehensive treatment of quantum optimization, Conditional Gradient Methods: From Core Principles to AI Applications
provides a rigorous introduction to the computational model of quantum computers,
contains detailed discussion of some of the most important developments in quantum optimization algorithms, and
summarizes the most important developments in the open literature.
The first comprehensive treatment of quantum optimization, Conditional Gradient Methods: From Core Principles to AI Applications
provides a rigorous introduction to the computational model of quantum computers,
contains detailed discussion of some of the most important developments in quantum optimization algorithms, and
summarizes the most important developments in the open literature.
More details
Series
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
ISBN-13
978-1-61197-875-9 (9781611978759)
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Schweitzer Classification
Person
Giacomo Nannicini is an associate professor in the Daniel J. Epstein Department of Industrial and Systems Engineering, with a courtesy appointment in the Ming Hsieh Department of Electrical and Computer Engineering in the USC School of Advanced Computing. He was a postdoctoral fellow at the CMU Tepper School of Business, a visiting scholar at the MIT Sloan School of Management, an assistant professor at the Singapore University of Technology and Design, and a research staff member at the IBM's T.J. Watson Research Center. He received the 2021 Beale-Orchard-Hays Prize, the 2016 COIN-OR Cup, the 2015 Robert Faure Prize, and the 2012 Glover-Klingman Prize. His main research and teaching interest is optimization and its applications.