EDGE course available both for on-campus and off-campus students
Recorded lectures can be downloaded from CANVAS course site
Introduction to Stochastic Optimization ESI 6341
M W F period 4 (10:40 - 11:30) at CSE E122
Professor: Stan Uryasev
446 Weil Hall
Tel: 352-294-7723
e-mail: uryasev@ufl.edu
Purpose of Course. Introduction to Stochastic Optimization is intended as a first introductory course for graduate students in such fields as engineering, operations research, statistics, mathematics, and business administration (in particular, finance or management science). The objective of the course is to help students build knowledge and intuition in decision making under the presence of uncertainties, including: 1) modeling of uncertainties; 2) changes which uncertainties bring to the decision process; 3) difficulties related to incorporation of uncertainties to optimization models; and 4) identifying of solvable problems.
The aim of stochastic programming techniques is to find an optimal decision in problems involving uncertainties and risks. The field, also known as optimization under uncertainty, is developing rapidly with contributions from many disciplines such as operations research, economics, statistics, and finance. Stochastic programming approaches have been successfully used in a number of areas
such as energy and production planning, telecommunications, forest and fishery harvest management, engineering, agriculture, and transportation. Recently, it was realized that practical experience accumulated in stochastic programming can be expanded to much larger spectrum of applications including financial modeling, risk management, and probabilistic risk analysis.
Topics to be covered:
- Various application examples: Capacity Expansion, Design for Manufacturing Quality, Rocket Design, Farming Planning, Financial Planning and Control, and Probabilistic Risk Analysis.
- Uncertainty and modeling issues (decisions and stages, two-stage programs, probabilistic programming, relationships to other decision-making models).
- Solution methods.
- Sensitivity analysis of stochastic systems (derivatives of expectations and probabilistic functions).
- Case studies.
Prerequisites: Some basic knowledge of calculus, statistics, and linear programming.
Text: John R. Birge and Francois Louveaux. "Introduction to Stochastic Programming". Springer, 2011. ISBN 978-1-4614-0236-7
This course is included in the list of required courses for the Ph.D. Program with Concentration in Quantitative Finance.
