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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
High-dimensional
Optimization Problem
Sketching
Nonconvex Optimization
Learning systems
Signal processing
Statistics
Predictive control systems
Recommender systems
Data mining
Learning systems
Statistics

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
Gradient
Optimization

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

Degradation
Testing
learning
Experiments
regime
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
Logistics
Deep neural networks