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7 February 2017 On 7 February 2017 Prof. Cao Xi-Ren, Chair Professor of Shanghai Jiao Tong University, gave a seminar on “Why Dynamic Programming Is Not Good Enough”. According to Prof. Cao, with its intrinsic weakness, dynamic programming fails to address many problems. It cannot solve, for example, the under selective issue of long-run average; i.e., the optimal policy does not depend on the actions in any finite period. Some of these problems, he mentioned, hinders the development of further research due to lack of insights. As a result, degenerate diffusions are not well investigated. Prof. Cao and his co-authors introduced an alternative approach called relative optimization, which is based on a direct comparison of the performance measures under any two policies, and it provides global information to the optimization problem. With this approach, optimality conditions that take under selectivity into consideration can be derived. For long-run average performance, the effect of non-smooth value function can be ignored. And for finite horizon problems and optimal stopping, optimality condition can be derived at the non-smooth points of the value function. Prof. Cao holds a PhD from Harvard University and has worked as a consulting engineer for Digital Equipment Corporation in the US, a Research Fellow at Harvard University, as well as a Reader, Professor, and Chair Professor at Hong Kong University of Science and Technology. His current research areas include stochastic control, financial engineering, stochastic learning and optimization, and discrete event dynamic systems.
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