Date & Time:
March 5, 2019 12:10 pm – 1:10 pm
Location:
Harper Center 3B, 5807 S. Woodlawn Ave., Chicago, IL,
03/05/2019 12:10 PM 03/05/2019 01:10 PM America/Chicago Jorge Nocedal (Northwestern) – Zero-Order Methods for the Optimization of Noisy Functions Joint University of Chicago and Toyota Technological Institute at Chicago Machine Learning Seminar Series Harper Center 3B, 5807 S. Woodlawn Ave., Chicago, IL,

Zero-Order Methods for the Optimization of Noisy Functions

This talk presents a finite difference quasi-Newton method for the minimization of noisy functions. The method takes advantage of the scalability and power of BFGS updating, and employs an adaptive procedure for choosing the differencing interval h based on the noise estimation techniques of Hamming and More and Wild. This noise estimation procedure and the selection of h are inexpensive but not always accurate, and to prevent failures the algorithm incorporates a recovery mechanism that takes appropriate action in the case when the line search procedure is unable to produce an acceptable point. A novel convergence analysis is presented that considers the effect of a noisy line search procedure. Numerical experiments comparing the method to a model based trust region method are presented.

Jorge Nocedal

Walter P. Murphy Professor, Northwestern University

My research interests are in optimization and its application in machine learning and in disciplines involving differential equations. I specialize in nonlinear optimization, both convex and non-convex; deterministic and stochastic. There is a need for solving ever larger optimization problems, and throughout the years, I have developed algorithms that scale well with the number of variables, make judicious use of second-order information, and parallelize well. The motivation for my current algorithmic and theoretical research stems from applications in image and speech recognition, recommendation systems, and search engines.

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