Grants per year
Personal profile
Research Interests
Professor Nocedal research interests are in optimization and its application in machine learning and in disciplines involving differential equations. He specializes in nonlinear optimization, both convex and non-convex; deterministic and stochastic. There is a need for solving ever larger optimization problems, and throughout the years, he has developed algorithms that scale well with the number of variables, make judicious use of second-order information, and parallelize well. The motivation for his current algorithmic and theoretical research stems from applications in image and speech recognition, recommendation systems, and search engines.
Education/Academic qualification
Mathematical Sciences, PhD, Rice University
… → 1978
Physics, BSc, National University of Mexico
… → 1974
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Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Machine Learning for Physics-based Systems: Optimal Approximations, Architectures, and Training
University of Texas at Austin, Air Force Office of Scientific Research
12/15/20 → 12/14/23
Project: Research project
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Zero-Order and Stochastic Methods for Large-Scale Optimization
8/1/20 → 7/31/23
Project: Research project
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Novel Methods for Stochastic Data-Driven Nonconvex Optimization
Space and Naval Warfare Systems Center Pacific
4/9/18 → 1/8/20
Project: Research project
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Analysis of the BFGS method with errors
Xie, Y., Byrd, R. H. & Nocedal, J., 2020, In: SIAM Journal on Optimization. 30, 1, p. 182-209 28 p.Research output: Contribution to journal › Article › peer-review
2 Scopus citations -
An investigation of Newton-Sketch and subsampled Newton methods
Berahas, A. S., Bollapragada, R. & Nocedal, J., Jul 3 2020, In: Optimization Methods and Software. 35, 4, p. 661-680 20 p.Research output: Contribution to journal › Article › peer-review
11 Scopus citations -
Derivative-free optimization of noisy functions via quasi-Newton methods
Berahas, A. S., Byrd, R. H. & Nocedal, J., 2019, In: SIAM Journal on Optimization. 29, 2, p. 965-993 29 p.Research output: Contribution to journal › Article › peer-review
14 Scopus citations -
Exact and inexact subsampled Newton methods for optimization
Bollapragada, R., Byrd, R. H. & Nocedal, J., Jan 1 2019, In: IMA Journal of Numerical Analysis. 39, 2, p. 545-548 4 p.Research output: Contribution to journal › Article › peer-review
29 Scopus citations -
Adaptive sampling strategies for stochastic optimization ∗
Bollapragada, R., Byrd, R. & Nocedal, J., 2018, In: SIAM Journal on Optimization. 28, 4, p. 3312-3343 32 p.Research output: Contribution to journal › Article › peer-review
15 Scopus citations