Date

2017

Document Type

Dissertation

Degree

Doctor of Philosophy

Department

Industrial Engineering

First Adviser

Scheinberg, Katya

Abstract

In recent years, there has been a tremendous increase in the interest of applying techniques of deterministic optimization to stochastic settings, largely motivated by problems that come from machine learning domains. A natural question that arises in light of this interest is the extent to which iterative algorithms designed for deterministic (nonlinear, possibly non-convex) optimization must be modified in order to properly make use of inherently random information about a problem.This thesis is concerned with exactly this question, and adapts the model-based trust-region framework of derivative-free optimization (DFO) for use in situations where objective function values or the set of points selected by an algorithm to be objectively evaluated are random.In the first part of this thesis, we consider an algorithmic framework called STORM (STochastic Optimization with Random Models), which as an iterative method, is essentially identical to model-based trust-region methods for smooth DFO. However, by imposing fairly general probabilistic conditions related to the concept of fully-linearity on objective function models and objective function estimates, we prove that iterates of algorithms in the STORM framework exhibit almost sure convergence to first-order stationary points for a broad class of unconstrained stochastic functions. We then show that algorithms in the STORM framework enjoy the canonical rate of convergence for unconstrained non-convex optimization. Throughout the thesis, examples are provided demonstrating how the mentioned probabilistic conditions might be satisfied through particular choices of model-building and function value estimation.In the second part of the thesis, we consider a framework called manifold sampling, intended for unconstrained DFO problems where the objective is nonsmooth, but enough is known a priori about the structure of the nonsmoothness that one can classify a given queried point as belonging to a certain smooth manifold of the objective surface. We particularly examine the case of sums of absolute values of (non-convex) black-box functions. Although we assume in this work that the individual black-box functions can be deterministically evaluated, we consider a variant of manifold sampling wherein random queries are made in each iteration to enhance the algorithm's ``awareness" of the diversity of manifolds in a neighborhood of a current iterate. We then combine the ideas of STORM and manifold sampling to yield a practical algorithm intended for non-convex $\ell_1$-regularized empirical risk minimization.

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