Stochastic Control With Affine Dynamics and Extended Quadratic Costs

An extended quadratic function is a quadratic function plus the indicator function of an affine set, i.e., a quadratic function with embedded linear equality constraints. In this article, we show that, under some technical conditions, random convex extended quadratic functions are closed under addition, composition with an affine function, expectation, and partial minimization, i.e., minimizing over some of its arguments. These properties imply that dynamic programming can be tractably carried out for stochastic control problems with random affine dynamics and extended quadratic cost functions. While the equations for the dynamic programming iterations are much more complicated than for traditional linear quadratic control, they are well suited to an object-oriented implementation, which we describe. We also describe a number of known and new applications.