  Approximate linear programming (ALP) and its variants have been widely
applied to Markov Decision Processes (MDPs) with a large number of states. A
serious limitation of ALP is that it has an intractable number of constraints,
as a result of which constraint approximations are of interest. In this paper,
we define a linearly relaxed approximation linear program (LRALP) that has a
tractable number of constraints, obtained as positive linear combinations of
the original constraints of the ALP. The main contribution is a novel
performance bound for LRALP.
