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PhD Defense by Yi Cheng
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Thesis Title: Risk Neutral and Risk Averse Stochastic Optimization
Thesis Committee:
Dr. Alexander Shapiro (advisor), School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Guanghui Lan (coadvisor), School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Alp Muharremoglu, Amazon.com
Dr. Lauren N. Steimle, School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Enlu Zhou, School of Industrial and Systems Engineering, Georgia Institute of Technology
Date and Time: Wednesday, November 23rd, 8:30 am (EST)
Location (Meeting Link): https://gatech.zoom.us/j/95280360071?pwd=WmZ4L1ozaCtUOEpKMDZ2VHVnK2xZdz09
Meeting ID: 952 8036 0071
Passcode: 817954
Abstract:
In this thesis, we focus on the modeling, computational methods and applications of multistage/singlestage stochastic optimization, which entail risk aversion under certain circumstances. Chapters 24 concentrate on multistage stochastic programming while Chapter 56 deal with a class of singlestage functional constrained stochastic optimization problems.
In Chapter 2, we investigate the deterministic upper bound of a Multistage Stochastic Linear Program (MSLP) and propose the Dual SDDP algorithm that computes a sequence of nonincreasing deterministic upper bounds for the optimal value of the problem, even without the presence of Relatively Complete Recourse. Sensitivity analysis of the problem parameters and application on an inventory problem are provided. We also propose the Periodical Dual SDDP algorithm that copes with the infinitehorizon MSLP and show its effectiveness in the upper bound construction when traditional statistical upper bound does not work. As a proof of concept of the developed tools, extensive numerical experiments are provided on the inventory problem and the Brazilian hydrothermal planning problems under both finitehorizon and infinitehorizon settings.
In Chapter 3, we propose a construction of the statistical upper bound for the optimal value of riskaverse Stochastic Optimal Control (SOC) problems. This outlines an approach to a solution of a long standing problem in that area of research. The bound holds for a large class of convex and monotone conditional risk mappings. We show the validity of the statistical upper bound to solve a realworld stochastic hydrothermal planning problem.
In Chapter 4, we address the sample complexity of solving stationary stochastic programs by the Sample Average Approximation (SAA) method. We investigate this in the framework of Stochastic Optimal Control setting. In particular, we derive a Central Limit Theorem type asymptotics for the optimal values of the SAA problems. The main conclusion is that the sample size, required to attain a given relative error of the SAA solution, is not sensitive to the discount factor, even if the discount factor is very close to one. We consider the risk neutral and risk averse settings. The presented numerical experiments confirm the theoretical analysis.
Chapter 5 is motivated by the requirement of simultaneously enforcing risk and sparsity in many important applications and the challenges thereof. To address the issues and overcome the computational difficulties, we propose a novel projectionfree method, referred to as Level Conditional Gradient (LCG) method, for solving convex functional constrained optimization. It leverages a levelset framework to update the estimation of the optimal value and an inner conditional gradient oracle (CGO) for solving minimax subproblems. In particular, the proposed CGO is capable of solving a general convex saddle point problem and provides computable lower and upper bounds of the problem. We show that the method achieves $\mathcal{O}\big(\frac{1}{\epsilon^2}\log\frac{1}{\epsilon}\big)$ iteration complexity for solving both smooth and nonsmooth cases without dependency on a possibly large magnitude of optimal dual Lagrange multiplier.
In Chapter 6, to cope with nonconvex functional constrained optimization, we introduce the Level Inexact Proximal Point (IPPLCG) method and the Direct Nonconvex Conditional Gradient (DNCG) method. The first approach taps into the advantage of LCG by transforming the problem into a series of convex subproblems and exhibits an $\mathcal{O}\big(\frac{1}{\epsilon^3}\log\frac{1}{\epsilon}\big)$ iteration complexity for finding an approximate KKT point. The DNCG is the first singleloop projectionfree method, with iteration complexity bounded by $\mathcal{O}\big(\frac{1}{\epsilon^4}\big)$ for computing a socalled $\epsilon$Wolfe point. Numerical study on a portfolio selection problem and the intensity modulated radiation therapy treatment planning problem shows all the proposed methods are efficient in jointly minimizing risk while promoting sparsity on the realworld and largescale datasets.
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Published 
Created By:
Tatianna Richardson 
Created:
11/09/2022 
Modified By:
Tatianna Richardson 
Modified:
11/09/2022
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