The Lagrange Prize in Continuous Optimization is awarded every three years by the Mathematical Optimization Society (MOS) and SIAM for an outstanding contribution in the area of continuous optimization published in the six calendar years preceding the award year.
The Lagrange Prize in Continuous Optimization is awarded every three years for an outstanding contribution in the area of continuous optimization published in the six calendar years prior to the award year. The MOS administers the prize and it is awarded jointly by MOS and SIAM.
The award is based primarily on the work's mathematical quality, significance, and originality. Clarity and excellence of the exposition and the value of the work in practical applications may be considered as secondary attributes. The extended period of six years reflects the fact that the value of fundamental work cannot always be immediately assessed.
The work must have been published as the final publication of the main result(s) within the six calendar years preceding the award year. The work should be published as an article in a peer-reviewed journal, or other peer-reviewed publication, intended to publish final papers only; or as a monograph publishing original results.
For the 2024 award, the work must have been published between the dates of January 1, 2018 – December 31, 2023.
Congratulations to the 2021 recipients, Léon Bottou, Frank E. Curtis, and Jorge Nocedal! Learn more about their accomplishments.
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The Lagrange Prize for Continuous Optimization includes a $1,500 monetary award and a certificate containing the citation. The recipient will be reimbursed for reasonable travel expenses incurred in attending the award ceremony.
The Lagrange Prize for Continuous Optimization will next be awarded at the 2024 International Symposium on Mathematical Programming.
The award is presented alternately at the International Symposium on Mathematical Programming (ISMP) and the SIAM Annual Meeting. The Chair of MOS or the SIAM President will announce the award and present the certificate to the recipient. The announcement of the award will appear in SIAM News, the SIAM website, and appropriate electronic media.
The MOS has the responsibility for soliciting and providing the funds necessary for the prize award.
The 2021 Lagrange Prize is awarded to Léon Bottou, Frank E. Curtis, and Jorge Nocedal for their paper, "Optimization Methods for Large-Scale Machine Learning", SIAM Review 60(2), 2018, which provides a foundational and insightful review of optimization methods for large-scale machine learning, including a new perspective for the simultaneous consideration of noise reduction and ill-conditioning and the foundations and analysis of second-order stochastic optimization methods for machine-learning.
Sven Leyffer (Chair)Xiaojun Chen Etienne de KlerkPhilip Gill
The 2018 Lagrange Prize is awarded to Francis Bach, Nicolas Le Roux, and Mark Schmidt for their paper, "Minimizing finite sums with the stochastic average gradient" (NIPS, 2012; Mathematical Programming, 2017).
This paper is the first in a series of significant advances in the design and analysis of stochastic gradient methods applied to finite-sum problems. In particular, it establishes that in this setting, the proposed variant of a stochastic gradient method achieves a linear convergence rate. This novel approach has resulted in a surge of interest in variance reduction methods that achieve superior performance compared to other first-order methods. A wide range of applications benefit from this methodology, including linear least squares, principal component analysis, and L1-regularization problems. In summary, this work represents a paradigm shift in theoretical analysis of stochastic methods applied to finite-sum optimization problems.
Katya Scheinberg (Chair) Etienne de Klerk Philip Gill Andreas Griewank
The 2015 Lagrange Prize is awarded to Andrew R. Conn, Katya Scheinberg, and Luis Nunes Vicente for their paper "Introduction to Derivative-Free Optimization", MPS-SIAM Series on Optimization, SIAM, 2009.
This monograph represents a significant contribution in understanding the formulation of surrogate models, the construction of derivative free optimization (DFO) algorithms, and their convergence properties. It includes a groundbreaking trust region framework for convergence that has made DFO both principled and practical. A large number of key optimization studies have used these results substantively in both practical and conceptual ways. This work has not only influenced new DFO algorithms; its results have also enabled the solution of numerous optimization applications in science and engineering. A small sampling of the direct impact of their work is seen in aerospace engineering, urban transport systems, adaptive meshing for partial differential equations, and groundwater remediation.
Mihai Anitescu (Chair) Kurt M. Anstreicher Lorenz Biegler Werner Roemisch
The 2012 Lagrange Prize is awarded to Emmanuel J. Candès and Benjamin Recht for their paper, "Exact matrix completion via convex optimization", Foundations of Computational Mathematics 9 (2009), 717-772.
The paper of Candès and Recht was selected because of its exposition excellence, the current importance of the topic and the impressive number of citations in three years. It also opens Semidefinite Optimization to a fascinating new field of applications and introduces a very clever mathematical approach for proving probabilistic tractability of certain NP hard problems.
Tamas Terlaky (Chair) Kurt M. Anstreicher Donald Goldfarb
Adrian S. Lewis (Chair) Jorge J. More Philippe L. Toint Margaret H. Wright Thomas M. Liebling
Michael J. Todd (Chair) John E. Dennis Jr. Nicholas I. Gould Adrian S. Lewis
C. Tim Kelley Claude Lemarechal Michael J. Todd Stephen J. Wright
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