John von Neumann Theory Prize
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2017 Winner(s)
- Donald Goldfarb, Columbia University
- Jorge Nocedal, Northwestern University
The award recognizes the seminal contributions that Donald Goldfarb and Jorge Nocedal have made to the theory and applications of nonlinear optimization over the past several decades. These contributions cover a full range of topics, going from modeling, to mathematical analysis, to breakthroughs in scientific computing. Their work on the variable metric methods (BFGS and L-BFGS, respectively) has been extremely influential.
Goldfarb’s deep research contributions tie together the theoretical and the very practical in traditional linear and nonlinear programming, interior point methods, and the newly in vogue methods developed for signal processing and machine learning; and doing all that through a unique understanding of the fundamental issues in each and all of these areas. His contributions to the field are exceptionally broad, very influential and long-lasting, beginning with the famous Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm for nonlinear optimization in the 60’s, then the revolutionary steepest edge simplex method for linear programming in the 80’s and in the last decade first-order methods for large-scale convex optimization. The primal and dual steepest edge simplex algorithms, devised by Goldfarb with Reid and Forrest, respectively, are the most widely used variants of the simplex method. Goldfarb’s work provides the theoretical foundation for many variants of this method implemented in most state-of-the-art commercial linear programming solvers. The Goldfarb-Idnani dual active set method for quadratic programming (QP) is one of the most widely used QP methods. One should also add that Goldfarb has a knack for exciting both talented PhD students and collaborators, to work on important research problems that he identified.
Nocedal made seminal contributions to the area of unconstrained and constrained nonlinear optimization that have fundamentally reshaped this field. This includes the development of L-BFGS methods, extending interior point methods to non-convex constrained optimization, co-authoring a highly influential book in nonlinear optimization, and recently illuminating the interface between optimization and machine learning via efficient and effective second-order methods. In the 1980s, Nocedal invented the L-BFGS optimization algorithm, the limited memory version of the BFGS method. This opened the door to solving vastly larger unconstrained and box-constrained nonlinear optimization problems than previously possible: Nocedal’s L-BFGS algorithm requires storage that is only a small multiple of the number of variables, whereas the original BFGS method required a quadratic amount of storage. The L-BFGS algorithm has had an immense practical impact, which is difficult to overstate. Nocedal was also instrumental in extending the interior-point revolution beyond convex optimization. In the late 1990s, he and his collaborators proposed the first theoretically sound algorithm for nonlinear and nonconvex optimization problems. This algorithm was practical and, importantly, did not rely on strong assumptions. It should also be added that, all throughout his career, Nocedal has been outstanding at mentoring both students and junior colleagues.
For their fundamental contributions, theoretical and practical, that have, and continue to have, a significant impact on the field of optimization, Donald Goldfarb and Jorge Nocedal are being awarded the John von Neumann Theory Prize of the Institute for Operations Research and the Management Sciences (INFORMS).
Purpose of the Award
2018 Committee Chair
Sunil Kumar
Johns Hopkins University
Baltimore, MD 21218
U.S.A.
email: lonathomas@jhu.edu
The John von Neumann Theory Prize is awarded annually to a scholar (or scholars in the case of joint work) who has made fundamental, sustained contributions to theory in operations research and the management sciences. The award is given each year at the INFORMS Annual Meeting if there is a suitable recipient. Although the Prize is normally given to a single individual, in the case of accumulated joint work, the recipients can be multiple individuals.
The Prize is awarded for a body of work, typically published over a period of several years. Although recent work should not be excluded, the Prize typically reflects contributions that have stood the test of time. The criteria for the Prize are broad, and include significance, innovation, depth, and scientific excellence.
The award is $5,000, a medallion and a citation.
Nominations due August 1, 2018
The Prize Committee is currently seeking nominations, which should be in the form of a letter (preferably email) addressed to the prize committee chair (below), highlighting the nominee's accomplishments. Although the letter need not contain a detailed account of the nominee's research, it should document the overall nature of his or her contributions and their impact on the profession, with particular emphasis on the prize's criteria. The nominee's curriculum vitae, while not mandatory, would be helpful.
About the Award/Namesake
John von Neumann was a brilliant mathematician, synthesizer, and promoter of the stored program concept, whose logical design of the IAS became the prototype of most of its successors - the von Neumann Architecture. von Neumann was invited to visit Princeton University in 1930, and when the Institute for Advanced Studies was founded there in 1933, he was appointed to be one of the original six Professors of Mathematics, a position which he retained for the remainder of his life. Postwar von Neumann concentrated on the development of the Institute for Advanced Studies (IAS) computer and its copies around the world. His work with the Los Alamos group continued and he continued to develop the synergism between computers capabilities and the needs for computational solutions to nuclear problems related to the hydrogen bomb.
Past Awardees
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