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Jorge Nocedal

  • 11312 Citations
1979 …2021

Research output per year

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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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Grants

Methods for High-Dimensional Nonlinear Optimization

Nocedal, J.

Office of Naval Research

2/1/18 → 1/31/21

Project: Research project

    • Collaborative Research: Algorithms for Large-Scale Stochastic and Nonlinear Optimization

    Nocedal, J.

    National Science Foundation

    8/1/16 → 7/31/20

    Project: Research project

    • Novel Methods for Stochastic Data-Driven Nonconvex Optimization

    Nocedal, J.

    Space and Naval Warfare Systems Center Pacific

    4/9/18 → 1/8/20

    Project: Research project

    • Large Scale Optimization Methods for Data Science Applications

    Nocedal, J.

    Office of Naval Research

    5/1/14 → 10/31/17

    Project: Research project

    • Collaborative Research: Methods for Stochastic and Nonlinear Optimization

    Nocedal, J.

    National Science Foundation

    8/1/12 → 7/31/16

    Project: Research project

    Research Output

    An investigation of Newton-Sketch and subsampled Newton methods

    Berahas, A. S., Bollapragada, R. & Nocedal, J., Jan 1 2020, (Accepted/In press) In : Optimization Methods and Software.

    Research output: Contribution to journal › Article

    • Derivative-free optimization of noisy functions via quasi-Newton methods

    Berahas, A. S., Byrd, R. H. & Nocedal, J., Jan 1 2019, In : SIAM Journal on Optimization. 29, 2, p. 965-993 29 p.

    Research output: Contribution to journal › Article

    • 2 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

    • 5 Scopus citations

    On large-batch training for deep learning: Generalization gap and sharp minima

    Keskar, N. S., Nocedal, J., Tang, P. T. P., Mudigere, D. & Smelyanskiy, M., Jan 1 2019.

    Research output: Contribution to conference › Paper

    • Adaptive sampling strategies for stochastic optimization ∗

    Bollapragada, R., Byrd, R. & Nocedal, J., Jan 1 2018, In : SIAM Journal on Optimization. 28, 4, p. 3312-3343 32 p.

    Research output: Contribution to journal › Article

    • 4 Scopus citations