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Filtering by: Creator Shi, Hao-Jun Michael Keyword Noisy optimization Resource type Dissertation Subject Applied mathematics

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Description:: Existing nonlinear optimization methods have proven reliable over the past few decades for a wide range of applications but have critically relied on accurate function and gradient evaluations. Modern nonlinear optimization problems arising from machine learning and scientific computing applications are increasingly complex and large scale, which make accurate evaluations...
Keyword:: Finite differences, Nonlinear optimization, Stochastic optimization, Noisy optimization, Quasi-Newton methods, and Derivative-free optimization
Subject:: Applied mathematics, Operations research, and Computer science
Creator:: Shi, Hao-Jun Michael
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/02/2022
Date Created:: 2021-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_848013 and http://dissertations.umi.com/northwestern:15785

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