Valuing programs with deterministic and stochastic cycles
In many dynamic programming problems, a mix of state variables exists - some exhibiting stochastic cycles and others having deterministic cycles. We derive a formula for the value function in infinite-horizon, stationary, Markovian decision problems by exploiting a special partitioned-circulant structure of the transition matrix [Pi]. Our strategy for computing the left-inverse of the matrix [I-[beta][Pi]], which is central to implementing Howard's policy iteration algorithm, yields significant improvements in computation time and major reductions in memory required. When the deterministic cycle is of order n, our cyclic inversion algorithm yields an O(n2) speed-up relative to the usual policy iteration algorithm.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
When requesting a correction, please mention this item's handle: RePEc:eee:dyncon:v:33:y:2009:i:3:p:614-623. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If references are entirely missing, you can add them using this form.