Faculty Directory
Jorge Nocedal

Walter P. Murphy Professor of Industrial Engineering and Management Sciences and (by courtesy) Engineering Sciences and Applied Mathematics

Director, Center for Optimization and Statistical Learning

Contact

2145 Sheridan Road
Tech E274
Evanston, IL 60208-3109

847-491-5038Email Jorge Nocedal

Website

Jorge Nocedal's Homepage


Departments

Industrial Engineering and Management Sciences


Download CV

Education

Ph.D. Mathematical Sciences, Rice University, Houston, TX

B.S. Physics, National University of Mexico, Mexico City, Mexico


Research Interests

Optimization, machine learning, optimal control, software, scientific computing.


Selected Publications

  • Nitish Shirish Keskar, Jorge Nocedal, Ping Tak Peter Tang, Dheevatsa Mudigere, Mikhail Smelyanskiy, “On large-batch training for deep learning”, , (2019)
  • Albert S. Berahas, Richard H. Byrd, Jorge Nocedal, “Derivative-free optimization of noisy functions via quasi-Newton methods”, SIAM Journal on Optimization, (2019)
  • Raghu Bollapragada, Richard H. Byrd, Jorge Nocedal, “Exact and inexact subsampled Newton methods for optimization”, IMA Journal of Numerical Analysis, (2018)
  • Lon Bottou, Frank E. Curtis, Jorge Nocedal, “Optimization methods for large-scale machine learning”, SIAM Review, (2018)
  • Albert S. Berahas, Jorge Nocedal, Martin Taká?, “A multi-batch L-BFGS method for machine learning”, Advances in Neural Information Processing Systems, (2016)
  • Richard H. Byrd, Gillian M. Chin, Jorge Nocedal, Figen Oztoprak, “A family of second-order methods for convex ?1 -regularized optimization”, Mathematical Programming, (2016)
  • N. Keskar, J. Nocedal, F. ztoprak, A. Wchter, “A second-order method for convex -regularized optimization with active-set prediction”, Optimization Methods and Software, (2016)
  • R. H. Byrd, S. L. Hansen, Jorge Nocedal, Y. Singer, “A stochastic quasi-Newton method for large-scale optimization”, SIAM Journal on Optimization, (2016)