Index Catalog // Arch : Northwestern University Institutional Repository

Description:: For stochastic simulation optimization in a modern computing era, we introduce a new parallel framework for solving very large-scale problems using a ranking & selection (R&S) approach that simulates all systems or feasible solutions to provide a global statistical guarantee. We propose a parallel adaptive survivor selection (PASS) framework that...
Keyword:: master-worker, simulation, parallel, stochastic, large-scale, and optimization
Subject:: Applied mathematics, Statistics, and Operations research
Creator:: Pei, Linda
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 06/22/2022
Date Created:: 2022-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_903888 and http://dissertations.umi.com/northwestern:16108

Description:: Recently, machine learning and deep learning, which have made many theoretical and empir- ical breakthroughs and is widely applied in various fields, attract a great number of researchers and practitioners. They have become one of the most popular research directions and plays a sig- nificant role in many fields, such...
Keyword:: Algorithm Design, Deep Learning, Optimization, and Machine Learning
Subject:: Applied mathematics and Operations research
Creator:: Fang, Biyi
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/02/2022
Date Created:: 2021-01-01
Resource Type:: Dissertation
Alternate Identifier:: http://dissertations.umi.com/northwestern:15817 and etdadmin_upload_855647

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

Description:: While optimization has received much attention in the machine learning community, most of them consider unconstrained supervised learning models such as neural networks and support vector machine. In this dissertation, we introduce a new class of optimization problems called scale invariant problems that include interesting unsupervised learning models such as...
Keyword:: Power Iteration, PCA, Optimization, Machine Learning, Scale Invariance, and KL-NMF
Subject:: Applied mathematics and Operations research
Creator:: Kim, Cheolmin
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/01/2021
Date Created:: 2020-01-01
Resource Type:: Dissertation
Alternate Identifier:: http://dissertations.umi.com/northwestern:15139 and etdadmin_upload_744178

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