Optimization of Black-Box Functions in the Presence of Noise (PD)

February 24, 2022
11:00 AM - 12:00 PM
Zoom

Description

Feb. 24: Jorge Nocedal (Northwestern University)

Abstract: In many engineering applications, one wishes to optimize the performance of a system simulated by software whose intrinsics are not accessible to us. Objective function values are available (and are typically noisy) but derivatives are unknown. We discuss how to solve problems of this kind in practice. Examples in machine learning and engineering design illustrate the challenges to be overcome, particularly when the number of unknowns is large and the model includes constraints that must be respected.

Zoom link: https://purdue-edu.zoom.us/j/97536401913

Contact Details

Event Website

https://www.math.purdue.edu/~yipn/IMS/

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