Ranking Templates for Linear Loops

We present a new method for the constraint-based synthesis of termination arguments for linear loop programs based on linear ranking templates. Linear ranking templates are parameterized, well-founded relations such that an assignment to the parameters gives rise to a ranking function. Our approach generalizes existing methods and enables us to use templates for many different ranking functions with affine-linear components. We discuss templates for multiphase, nested, piecewise, parallel, and lexicographic ranking functions. These ranking templates can be combined to form more powerful templates. Because these ranking templates require both strict and non-strict inequalities, we use Motzkin's transposition theorem instead of Farkas' lemma to transform the generated $\exists\forall$-constraint into an $\exists$-constraint.