Randomized optimization for stochastic systems: Theory and applications.
Event details
| Date | 23.10.2009 |
| Hour | 10:15 |
| Speaker | Prof. J. Lygeros, Automatic Control Laboratory (ETHZ, Zürich). |
| Location |
MEC2405
|
| Category | Conferences - Seminars |
Simulated annealing, Markov Chain Monte Carlo, and genetic algorithms are all randomized
methods that can be used in practice to solve (albeit approximately) complex optimization
problems. They rely on constructing appropriate Markov chains, whose stationary distribution
concentrates on "good" parts of the parameter space (i.e. near the optimizers). Many of these
methods come with asymptotic convergence guarantees that establish conditions under
which the Markov chain converges to a globally optimal solution in an appropriate
probabilistic sense. An interesting question that is usually not covered by asymptotic
convergence results is the rate of convergence: How long should the randomized algorithm
be executed to obtain a near optimal solution with high probability? Answering this question
allows one to determine a level of accuracy and confidence with which approximate
optimality claims can be made, as a function of the amount of time available for
computation. In this talk we present some new results on finite sample bounds of this type,
primarily in the context of stochastic optimization with expected value criteria using Markov
Chain Monte Carlo methods. The discussion will be motivated by the application of these
methods to collision avoidance in air traffic management and parameter estimation for
biological systems.
Links
Practical information
- General public
- Free