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About this Digital Document
In the first chapter of this thesis, we analyze the global convergence rate of a proximal quasi-Newton algorithm for solving composite optimization problems, in both exact and inexact settings, in the case when the objective function is strongly convex. W
Full Title
Optimization Algorithms for Machine Learning Problems
Member of
Contributor(s)
Creator: Ghanbari, Hiva
Thesis advisor: Scheinberg, Katya
Publisher
Lehigh University
Date Issued
2019-05
Language
English
Type
Genre
Form
electronic documents
Department name
Industrial Engineering
Digital Format
electronic documents
Media type
Creator role
Graduate Student
Subject (LCSH)
Ghanbari, . H. (2019). Optimization Algorithms for Machine Learning Problems (1–). https://preserve.lehigh.edu/lehigh-scholarship/graduate-publications-theses-dissertations/theses-dissertations/optimization-9
Ghanbari, Hiva. 2019. “Optimization Algorithms for Machine Learning Problems”. https://preserve.lehigh.edu/lehigh-scholarship/graduate-publications-theses-dissertations/theses-dissertations/optimization-9.
Ghanbari, Hiva. Optimization Algorithms for Machine Learning Problems. May 2019, https://preserve.lehigh.edu/lehigh-scholarship/graduate-publications-theses-dissertations/theses-dissertations/optimization-9.