MA662 Stochastic Optimization
Course Catalog Description
Objective
Instructors
Professor | Office | |
---|---|---|
Darinka Dentcheva | darinka.dentcheva@stevens.edu |
More Information
Course Outcomes
1. Ability to formulate an appropriate optimization model for a decision making
problem involving uncertain parameters.
2. Knowledge of the mathematical models of stochastic optimization; their structure,
advantages, and limitations.
3. Knowledge of the mathematical models of risk-averse optimization and the relations
between them.
4. Knowledge of the numerical approaches for solving problems with constraints on
probability of events, stochastic-order constraints, two- and multi-stage problems.
5. Understanding of time-consistency for dynamic risk.
6. Understanding the statistical meaning of the optimal value and solution for
stochastic optimization problems based on sampled data.
Course Materials
There is no required textbook; lecture notes will be distributed in class. The following are supplementary books:
- A. Shapiro, D. Dentcheva, A. Ruszczynski: Lecture Notes on Stochastic Programming Modeling and Theory, SIAM and MPS, Second Edition 2014.The first edition is available online.
- A. Ruszczynski and A. Shapiro, Stochastic Programming, Handbook in Operations Research and Management Science, Elsevier Science, Amsterdam, 2003
- Andras Prekopa, Stochastic Programming, Kluwer Academic Publishers, Netherlands, 1995
Grading
Grading Policies
A student's course grade will be based on 7-8 assignments.
Lecture Outline
Date | Topic | Reading |
---|---|---|
Week 1 | Modeling issues in presence of uncertainty and risk. | |
Week 2 | Optimization problem with probabilistic (chance) constraints: properties. | |
Week 3 | Numerical solution of optimization problems with probabilistic constraints. | |
Week 4 | Calculating bounds on probability of events. | |
Week 5 | Stochastic optimization with recourse. Properties of the expected value functional. The structure of stochastic models with recourse. | |
Week 6 | Numerical methods for solving two-stage problems. | |
Week 7 | Multistage stochastic problems and their properties. | |
Week 8 | Numerical techniques for multistage models. | |
Week 9 | Risk-averse optimization:stochastic-order constraints. | |
Week 10 | Numerical methods for problem with stochastic-order constraints | |
Week 11 | Optimization problems with coherent measures of risk. | |
Week 12 | Dynamic measures of risk. Time consistency. | |
Week 13 | Optimization of dynamic measures of risk. | |
Week 14 | Statistical aspects of stochastic optimization models. |