Dynamic programming via the quadratic transform

With a view to generalizing existing max-plus and min-plus methods so as to use quadratic basis functions with a non-uniform Hessian, the underlying dual space representation of dynamic programming founded on the semiconvex transform is generalized via the quadratic transform. Using this generalization, an illustrative iteration is proposed as the foundation of a new max-plus method for the solution of a class of optimal control problems.