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Filtering by: Creator Shi, Hao-Jun Michael Type Work

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Methods for Stochastic, Noisy, and Derivative-Free Optimization

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