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1. Second-Order Methods for Stochastic and Nonsmooth Optimization

Description:: The goal of this thesis is to design practical algorithms for nonlinear optimization in the case when the objective function is stochastic or nonsmooth. The thesis is divided into three chapters. Chapter 1 describes an active-set method for the minimization of an objective function that is structurally nonsmooth, viz., it...
Keyword:: machine learning, optimization, deep learning, Newton, nonsmooth, and quasi-Newton
Subject:: Industrial Engineering and Management Sciences
Creator:: Nitish Shirish Keskar
Owner:: Scholarly Digital Publishing
Date Uploaded:: 04/16/2018
Date Modified:: 04/16/2018
Date Created:: 2017-01-01
Resource Type:: Dissertation

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1. Second-Order Methods for Stochastic and Nonsmooth Optimization