PHILADELPHIA, PA — In a remarkable testament to his groundbreaking work in nonlinear optimization, Northwestern University’s Jorge Nocedal was recently awarded the distinguished 2024 John von Neumann Prize. This award, conferred by the Society for Industrial and Applied Mathematics (SIAM), is the highest honor in the field and includes a flagsahip lecture. Nocedal’s recognition comes after years of significant contributions to the deterministic and stochastic contexts of nonlinear optimization. The award ceremony is set to take place at the SIAM Annual Meeting on Tuesday, July 9, 2024.
Nocedal’s research work comprises seminal contributions to quasi-Newton methods, interior-point methods, and the theoretical basis of stochastic gradient methods that are instrumental to machine learning. Under his leadership, two highly influential software products came into existence – L-BFGS-B and KNITRO. These applications continue to command influence in a wide range of disciplines. Another feather in his cap is his co-authored textbook, Numerical Optimization, which has become a cornerstone in the realm of applied mathematics.
The John von Neumann Prize is awarded annually by SIAM to an individual with outstanding contributions to the field of applied mathematics. The receiver is required to have effectively communicated these ideas to the community, a feat Nocedal has achieved remarkably.
Born in Mexico City to parents who rose from poverty to the upper-middle class, Nocedal’s interest in optimization took root during his freshman year at the National Autonomous University of Mexico. Here, an internship at the Astronomy Institute saw him design a new telescope using an optimization program, sparking his lifelong pursuit of this field.
Nocedal’s journey led him to Rice University, where he obtained a Ph.D. in mathematical sciences before returning to Mexico to teach at his alma mater, UNAM. Following this, he found himself at the Courant Institute of Mathematical Sciences in New York as a research associate. After marriage, he took the next logical step of a job at Northwestern University, drawn by its close proximity to Argonne National Laboratory. It was here that he met Stephen J. Wright, leading to their collaborative textbook.
At Northwestern University, Nocedal has made significant strides in his career. He is the Walter P. Murphy Professor in Industrial Engineering and Management Sciences and Applied Mathematics and the Director of the Center for Optimization and Statistical Learning. He transitioned from the Electrical and Computer Engineering Department to the Department of Industrial Engineering and Management Sciences (IEMS), ensuring optimization work at Northwestern was streamlined in one department. He later became the Department Chair of IEMS, a role he found incredibly rewarding.
The optimization field has seen considerable progress, much of which Nocedal proudly contributes to. Issues that were once considered challenging, like a problem with a thousand decision variables, are now solvable even with millions of variables amid uncertainty. These advances are attributed to a better understanding of the inner workings of algorithms – their strengths, their failures, and their incredible resilience.
Nocedal has been a SIAM member for 42 years and considers it his primary research society. His active participation in SIAM includes being named a 2010 SIAM Fellow, co-founding the SIAM Journal on Optimization, and serving as an associate editor, culminating in a stint as editor-in-chief. His dedication also earned him the 2012 George B. Dantzig Prize and 2021 Lagrange Prize in Continuous Optimization.
Established in 1959, the John von Neumann Prize honors the seminal work of the Hungarian American mathematician, physicist, and computer scientist, John von Neumann, a key figure in the inception of modern computing. Nocedal will receive his award and deliver the lecture at the 2024 SIAM Annual Meeting, scheduled from July 8-12, 2024, in Spokane, Washington. The ceremonial talk is slated for July 9 at 2:30 p.m. Pacific Daylight Time.
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