3/2/2017 | 4:15pm | E51-335
Reception to follow.

Giacomo Nannicini

Research Staff Member
IBM

Abstract: 
We study the problem of approximating the optimal value and the optimal policy of a stochastic dynamic program with continuous state and action spaces. This problem is harder than its discrete counterpart and does not admit neither additive nor multiplicative approximation in the usual sense, but if the dynamic program satisfies certain structural requirements, we can efficiently compute an approximation with arbitrarily small additive and multiplicative error (combined). This result holds under a very general implicit model for the representation of random events, yielding excellent computational performance as compared to existing methodologies on a set of benchmark instances. We exemplify the usefulness of this approximation framework by applying it to the continuous nonlinear newsvendor problem, and to multistage stochastic linear programs with scalar state.

Based on joint work with Nir Halman

Bio: Giacomo Nannicini is a research staff member in the Quantum Algorithms and Software group at IBM T.J. Watson. He received a Ph.D. from Ecole Polytechnique in France, and he worked as a postdoctoral research fellow at CMU Tepper and MIT Sloan. From 2011 to 2016, he was an Assistant Professor in the Engineering Systems & Design pillar at the Singapore University of Technology and Design. His main research interest is mathematical optimization and its applications. He received the 2012 Glover-Klingman prize, the 2015 Prix Robert Faure, the 2016 COIN-OR Cup, and one of his papers was selected for the 10 Year Virtual Special Issue of Discrete Optimization. He is a technical editor for Mathematical Programming Computation.