Wednesday, December 21, 2022 - 15:00 in V10-122
Solving Optimal Stopping Problems via Randomization and Empirical Dual Optimization
A talk in the Mathematical finance / Insurance mathematics series by
Christian Bender from Universität des Saarlandes
| Abstract: |
In this talk, we consider optimal stopping problems in their dual form. In this way, the optimal stopping problem can be reformulated as a problem of sample average approximation (SAA) that can be solved via linear programming. By randomizing the initial value of the underlying process, we enforce solutions with zero variance while preserving the linear programming structure of the problem. A careful analysis of the randomized SAA algorithm shows that it enjoys favorable properties such as faster convergence rates and reduced complexity compared to the nonrandomized procedure. We illustrate the performance of our algorithm on several benchmark examples.
The talk is based on joint work with D. Belomestny (U Duisburg-Essen) and John Schoenmakers (WIAS Berlin).
Within the CRC this talk is associated to the project(s): C4 |
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