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


2145 Sheridan Road
Tech E274
Evanston, IL 60208-3109

847-491-5038Email Jorge Nocedal


Jorge Nocedal's Homepage


Industrial Engineering and Management Sciences

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

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

Research Interests

Main area of research is optimization, with applications in machine learning, engineering design, and power systems. Research activities range from the design of new algorithms, to their software implementation and mathematical analysis. Areas of emphasis include large scale problems (with millions of variables), optimization under uncertainty, and parallel computing.

Selected Publications

  • Berahas, Albert S.; Bollapragada, Raghu; Nocedal, Jorge, An investigation of Newton-Sketch and subsampled Newton methods, Optimization Methods and Software 35(4):661-680.
  • Xie, Yuchen; Byrd, Richard H.; Nocedal, Jorge, Analysis of the BFGS method with errors, SIAM Journal on Optimization 30(1):182-209.
  • Bollapragada, Raghu; Byrd, Richard H.; Nocedal, Jorge, Exact and inexact subsampled Newton methods for optimization, IMA Journal of Numerical Analysis 39(2):545-548.
  • Berahas, Albert S.; Byrd, Richard H.; Nocedal, Jorge, Derivative-free optimization of noisy functions via quasi-Newton methods, SIAM Journal on Optimization 29(2):965-993.
  • Bollapragada, Raghu; Mudigere, Dheevatsa; Nocedal, Jorge; Shi, Hao Jun Michael; Tang, Ping Tak Peter, A Progressive Batching L-BFGS Method for Machine Learning, International Machine Learning Society (IMLS):989-1013.
  • Bollapragada, Raghu; Byrd, Richard; Nocedal, Jorge, Adaptive sampling strategies for stochastic optimization , SIAM Journal on Optimization 28(4):3312-3343.
  • Bottou, Léon; Curtis, Frank E.; Nocedal, Jorge, Optimization methods for large-scale machine learning, SIAM Review 60(2):223-311.
  • Keskar, N.; Nocedal, J.; Öztoprak, F.; Wächter, A., A second-order method for convex ℓ1-regularized optimization with active-set prediction, Optimization Methods and Software 31(3):605-621.