Math/AMath 517: Optimization Under Uncertainty
A First Course in Stochastic Programming
Lisa Korf
Spring 2000, MWF 12:30 - 1:20
Almost every practical decision is made in the midst of some uncertainty
about what will take place in the future that might affect the "value"
of that decision. How does one formulate such a problem mathematically?
The stochastic programming approach to decision making under uncertainty
assigns a probability distribution to the uncertain parameters in an optimization
problem. This approach is robust because it takes into account the effects
of the possible future outcomes. However, such problems are inherently
very large, so understanding and exploiting their underlying structure
becomes very important from the perspective of computation.
In this course, we will address how to practically model various types
of problems in a structural optimization framework that may eventually
be exploited in computational schemes. Theory and algorithms will be emphasized,
and students will have a chance to model and solve practical problems to
gain insight into the current capabilities in this quickly growing field.
Throughout the class, the emphasis will be on applications to such diverse
and interesting areas as capacity planning, inventory control, vehicle
routing, water resources, forestry, energy, and finance.
Class notes and further information are available on the class web page:
http://www.math.washington.edu/~korf/classes/517/517.html.