Grants per year
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
Conjugate gradient method
Engineering & Materials Science
Quadratic programming
Engineering & Materials Science
Quasi-Newton Method
Mathematics
Nonlinear Optimization
Mathematics
Nonlinear programming
Engineering & Materials Science
Network
Recent external collaboration on country level. Dive into details by clicking on the dots.
Grants 2002 2021
An Iterative Approach for Solving the SCOPF Problem Applying LP, SOCP and NLP Subproblems
Advanced Research Projects Agency - Energy
1/8/19 → 1/7/20
Project: Research project
Experiments
Novel Methods for Stochastic Data-Driven Nonconvex Optimization
Space and Naval Warfare Systems Center Pacific
4/9/18 → 10/8/19
Project: Research project
Face recognition
Speech recognition
Learning systems
Deep neural networks
Nonlinear Optimization
High-dimensional
Optimization Problem
Sketching
Nonconvex Optimization
Collaborative Research: Algorithms for Large-Scale Stochastic and Nonlinear Optimization
8/1/16 → 7/31/20
Project: Research project
Learning systems
Signal processing
Statistics
Large Scale Optimization Methods for Data Science Applications
5/1/14 → 10/31/17
Project: Research project
Predictive control systems
Recommender systems
Data mining
Learning systems
Statistics
Research Output 1979 2019
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
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 journal › Article
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 conference › Paper
Degradation
Testing
learning
Experiments
regime
1
Citation
(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 journal › Article
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 proceeding › Conference contribution
Learning systems
Logistics
Deep neural networks