The Optimal Reward Operator in Dynamic Programming

Risk-Reward Analysis in Stochastic Dynamic Programming

Stochastic dynamic programming models are extensively used for sequential decision making when outcomes are uncertain. These models have been widely applied in different business contexts such as inventory control, capacity expansion, cash management, etc. The objective in these models is to deduce optimal policies based on expected reward criteria. However, in many cases, managers are concerne...

Optimal radial contour tracking by dynamic programming

A common problem in most active contour methods is that the recursive searching scheme can only return a local optimal solution. Furthermore, the internal energy of the snake is not strong enough to control the shape of the contour. To overcome these difficulties, in this paper, we develop a causal internal energy term based on a radial contour representation to encode the smooth constraint of ...

Finding Optimal Bayesian Networks by Dynamic Programming

Finding the Bayesian network that maximizes a score function is known as structure learning or structure discovery. Most approaches use local search in the space of acyclic digraphs, which is prone to local maxima. Exhaustive enumeration requires super-exponential time. In this paper we describe a "merely" exponential space/time algorithm for finding a Bayesian network that corresponds to a glo...

Optimal arrival traffic spacing via dynamic programming∗

We present the application of dynamic programming to a combinatorial optimization problem to achieve proper arrival runway spacing, which appears in the process of assigning speed during the transition to approach and approach phases of flight. We apply the algorithm to data from a fast-time simulation developed under NASA’s Advanced Air Transportation Technologies Project for investigating new...

Dynamic Programming and Optimal Motion Planning