Jorge Nocedal, Northwestern University, has been awarded the 2024 John von Neumann Prize – the highest honor and flagship lecture of Society for Industrial and Applied Mathematics (SIAM) – in recognition of his fundamental work in nonlinear optimization, both in the deterministic and stochastic settings. The talk is happening Tuesday, July 9 at the 2024 SIAM Annual Meeting.
Nocedal’s research comprises numerous contributions to quasi-Newton methods, interior-point methods, and the theoretical foundations of stochastic gradient methods that are pivotal to machine learning. His leadership resulted in the creation of L-BFGS-B and KNITRO, two software products that remain highly influential in a broad range of applications. He is the co-author of the distinguished textbook Numerical Optimization, which has become a modern classic in applied mathematics.
SIAM awards the John von Neumann Prize annually to an individual for outstanding and distinguished contributions to the field of applied mathematics and for the effective communication of these ideas to the community. It is one of SIAM’s most distinguished prizes.
“I am very excited to receive such a prestigious award,” Nocedal said. “Optimization is everywhere. It drives weather forecasts and creates machine learning models. I am very happy to see that my algorithms and software are used in dozens of disciplines, including many outside science and engineering.”
Nocedal was born in Mexico City. His parents grew up in great poverty and worked their way into the upper middle class. He studied physics at the National Autonomous University of Mexico. During his freshman year, he interned in the Astronomy Institute, where he was assigned to help design a new telescope using an optimization program. In this fortuitous manner, he was exposed to the field that he would pursue throughout his whole career: optimization.
Nocedal received a Ph.D. in mathematical sciences at Rice University before returning to Mexico to teach at his alma mater, UNAM, for three years. Following that, he moved to the Courant Institute of Mathematical Sciences in New York where he became a research associate. He never consciously planned to stay in the U.S., but after getting married while in New York, the next logical step for Nocedal was to take a job at Northwestern University, particularly because of its appealing proximity to Argonne National Laboratory. It was there that he met Stephen J. Wright, who would become his book co-author.
Nocedal 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 at Northwestern University. His association with Northwestern has been a happy one, as the university has been on an upwards trajectory for decades. He moved from the Electrical and Computer Engineering Department to the Department of Industrial Engineering and Management Sciences (IEMS), so that all optimization work at Northwestern would be consolidated there. He became Department Chair of IEMS, which was a very rewarding experience for him.
Nocedal always thought that he would work on optimization for a few years and then move onto PDEs or some other field, but optimization has blossomed over the years, and he has found plenty of opportunities and challenges, such as those created by the rise of machine learning. A constant presence in his career has been his colleague Richard Byrd, with whom he has written many papers and regards as one of the most brilliant people he has met. Just as important to his career have been his students and postdocs, who deserve much credit for the software he developed with them over the years. Nocedal considers teaching to be a great privilege and is extremely proud that he has kept in close contact with most of his students, thus creating a true extended family. Learn more about Nocedal.
“Advances in the field have been gigantic and I am proud that I contributed a bit. A problem with 1,000 decision variables was considered very challenging when I was a student, whereas now we can solve problems with millions of variables, even amid uncertainty. That triple combination of high dimensionality, nonlinearity, and uncertainty seemed insurmountable at some point, yet now, we have a better understanding of what makes some algorithms fail and what makes others amazingly resilient. These advances would not have been made possible if I had not considered theory, algorithmic design, and software development as three indispensable components,” Nocedal said.
As a SIAM member for 42 years, Nocedal regards SIAM as his primary research society. He has actively participated in SIAM for decades, including being named a 2010 SIAM Fellow, as well as co-founding the SIAM Journal on Optimization and serving as an associate editor (1990-2014) and editor-in-chief (2010-14). He also served as an associate editor of SIAM Review (2006-09) and was awarded the 2012 George B. Dantzig Prize and 2021 Lagrange Prize in Continuous Optimization. Additionally, Nocedal has served on the SIAM Outstanding Paper Prizes Committee (2007-08), the George B. Dantzig Prize Committee (2017-18; 2020-21), and the SIAM Fellows Selection Committee (2018-20).
This prize was established in 1959 to honor John von Neumann, a Hungarian American mathematician, physicist, and computer scientist, whose seminal work helped lead to the founding of modern computing. Learn more about SIAM’s John von Neumann Prize.
Nocedal will be awarded the John von Neumann Prize and deliver the associated lecture at the 2024 SIAM Annual Meeting, which will be held July 8-12, 2024, in Spokane, Washington. The talk is happening July 9 at 2:30 p.m. Pacific Daylight Time (subject to change).