ABSTRACT Title of dissertation: SIMULATION-BASED METHODS FOR STOCHASTIC CONTROL AND GLOBAL OPTIMIZATION

Ideas of stochastic control have found applications in a variety of areas. A subclass of the problems with parameterized policies (including some stochastic impulse control problems) has received significant attention recently because of emerging applications in the areas of engineering, management, and mathematical finance. However, explicit solutions for this type of stochastic control problems only exist for some special cases, and effective numerical methods are relatively rare. Deriving efficient stochastic derivative estimators for payoff functions with discontinuities arising in many problems of practical interest is very challenging. Global optimization problems are extremely hard to solve due to the typical multimodal properties of objective functions. With the increasing availability of computing power and memory, there is a rapid development in the merging of simulation and optimization techniques. Developing new and efficient simulation-based optimization algorithms for solving stochastic control and global optimization problems is the primary goal of this thesis. First we develop a new simulation-based optimization algorithm to solve a stochastic control problem with a parameterized policy that arises in the setting of dynamic pricin