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Personal profile

Education/Academic qualification

Mathematical Sciences, PhD, Rice University

… → 1978

Physics, BSc, National University of Mexico

… → 1974

Fingerprint Dive into the research topics where Jorge Nocedal is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

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Newton-Raphson method Engineering & Materials Science
Constrained optimization Engineering & Materials Science
Constrained Optimization Mathematics
Quadratic programming Engineering & Materials Science
Quasi-Newton Method Mathematics
Nonlinear Optimization Mathematics
Nonlinear programming Engineering & Materials Science
Conjugate gradient method Engineering & Materials Science

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Grants 2002 2021

Face recognition
Speech recognition
Learning systems
Deep neural networks
Nonlinear Optimization
Optimization Problem
Nonconvex Optimization
Learning systems
Signal processing
Predictive control systems
Recommender systems
Data mining
Learning systems

Research Output 1979 2019

1 Citation (Scopus)

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 journalArticle

Derivative-free Optimization
Quasi-Newton Method
Newton-Raphson method
Noise Estimation
Line Search
2 Citations (Scopus)

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 journalArticle

Inexact Newton Methods
Conjugate gradient method
Newton-Raphson method

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 conferencePaper

2 Citations (Scopus)

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 journalArticle

Adaptive Sampling
Sampling Strategy
Adaptive Strategies
Stochastic Optimization
Sample Size

A Progressive Batching L-BFGS Method for Machine Learning

Bollapragada, R., Mudigere, D., Nocedal, J., Shi, H. J. M. & Tang, P. T. P., Jan 1 2018, 35th International Conference on Machine Learning, ICML 2018. Dy, J. & Krause, A. (eds.). International Machine Learning Society (IMLS), Vol. 2. p. 989-1013 25 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Learning systems
Deep neural networks