Dimitri Bertsekas

Bertsekas was born in Greece and lived his childhood there. He studied for five years at the National Technical University of Athens, Greece and studied for about a year and a half at The George Washington University, Washington, D.C., where he obtained his M.S. in electrical engineering in 1969, and for about two years at MIT, where he obtained his doctorate in system science in 1971. Prior to joining the MIT faculty in 1979, he taught for three years at the Engineering-Economic Systems Dept. of Stanford University, and for five years at the Electrical and Computer Engineering Dept. of the University of Illinois at Urbana-Champaign. In 2019, he was appointed a full-time professor at the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University, Tempe, while maintaining a research position at MIT.[4][5]

He is known for his research work, and for his seventeen textbooks and monographs in theoretical and algorithmic optimization and control, and in applied probability. His work ranges from theoretical/foundational work, to algorithmic analysis and design for optimization problems, and to applications such as data communication and transportation networks, and electric power generation. He is featured among the top 100 most cited computer science authors[6] in the CiteSeer search engine academic database[7] and digital library.[8] In 1995, he co-founded a publishing company, Athena Scientific, that among others, publishes most of his books.

In the late 1990s Bertsekas developed a strong interest in digital photography. His photographs have been exhibited on several occasions at MIT.[9]

Bertsekas was awarded the INFORMS 1997 Prize for Research Excellence in the Interface Between Operations Research and Computer Science[10] for his book "Neuro-Dynamic Programming" (co-authored with John N. Tsitsiklis); the 2000 Greek National Award for Operations Research; and the 2001 ACC John R. Ragazzini Education Award for outstanding contributions to education.[11] In 2001, he was elected to the US National Academy of Engineering for "pioneering contributions to fundamental research, practice and education of optimization/control theory, and especially its application to data communication networks".[12] In 2009, he was awarded the 2009 INFORMS Expository Writing Award for his ability to "communicate difficult mathematical concepts with unusual clarity, thereby reaching a broad audience across many disciplines. "[13] In 2014 he received the Richard E. Bellman Control Heritage Award from the American Automatic Control Council,[14][15] the Khachiyan Prize for life-time achievements in the area of optimization from the INFORMS Optimization Society.,[16] the 2015 Dantzig prize from SIAM and the Mathematical Optimization Society,[17] and the 2018 INFORMS John von Neumann Theory Prize (jointly with Tsitsiklis) for the books "Neuro-Dynamic Programming" and "Parallel and Distributed Algorithms".

Bertsekas' textbooks include

• Dynamic Programming and Optimal Control (1996)
• Data Networks (1989, co-authored with Robert G. Gallager)
• Nonlinear Programming (1996)
• Introduction to Probability (2003, co-authored with John N. Tsitsiklis)
• Convex Optimization Algorithms (2015)

all of which are used for classroom instruction at MIT.[18][19] Some of these books have been published in multiple editions, and have been translated in various foreign languages.

He has also written several research monographs,[20] which collectively contain most of his research. These include:

• "Stochastic Optimal Control: The Discrete-Time Case" (1978, co-authored with S. E. Shreve), a mathematically complex work, establishing the measure-theoretic foundations of dynamic programming and stochastic control.
• "Constrained Optimization and Lagrange Multiplier Methods" (1982), the first monograph that addressed comprehensively the algorithmic convergence issues around augmented Lagrangian and sequential quadratic programming methods.
• "Parallel and Distributed Computation: Numerical Methods" (1989, co-authored with John N. Tsitsiklis), which among others established the fundamental theoretical structures for the analysis of distributed asynchronous algorithms.
• "Linear Network Optimization" (1991) and "Network Optimization: Continuous and Discrete Models" (1998), which among others discuss comprehensively the class of auction algorithms for assignment and network flow optimization, developed by Bertsekas over a period of 20 years starting in 1979.
• "Neuro-Dynamic Programming"(1996, co-authored with Tsitsiklis), which laid the theoretical foundations for suboptimal approximations of highly complex sequential decision-making problems.
• "Convex Analysis and Optimization" (2003, co-authored with A. Nedic and A. Ozdaglar) and Convex Optimization Theory (2009), which provided a new line of development for optimization duality theory, a new connection between the theory of Lagrange multipliers and nonsmooth analysis, and a comprehensive development of incremental subgradient methods.
• "Abstract Dynamic Programming" (2013), which aims at a unified development of the core theory and algorithms of total cost sequential decision problems, based on the strong connections of the subject with fixed point theory. A 2nd edition of this monograph, which includes most of his research on dynamic programming in the period 2013-2017, appeared in 2018.

His latest research monograph is Reinforcement Learning and Optimal Control (2019), which aims to explore the common boundary between dynamic programming/optimal control and artificial intelligence, and to form a bridge that is accessible by workers with background in either field.

• Neural network
• Reinforcement learning
• Data network
• List of convexity topics

• Publications from Google Scholar.
• Publications from DBLP.
• Biography from National Academy of Engineering
• Bertsekas' home page at MIT
• Athena Scientific
• Laboratory for Information and Control Systems, MIT
• Department of Electrical Engineering and Computer Science, MIT
• Biography of Dimitri Bertsekas from the Institute for Operations Research and the Management Sciences (INFORMS)