# Welcome

QNet Approximator consists of four programs that compute lower and upper bounds on the optimal average cost for a class of stochastic processing networks by solving an approximate linear program (ALP). A fifth program generates policies from ALP solutions.

ALPs approximate the differential cost function as a linear combination of some set of basis functions. Supported basis functions include

- linear functions of
`x`, - quadratic functions of
`x`, - certain exponential of
`x`, - indicator functions for certain states
`x`, and - user-defined functions of
`x`.

This software constructs linear programs which can be solved using CPLEX, if available, or with GLPK, a freely available open-source linear programming toolkit.

This software was developed with support from NSF Grant #CMMI-0620787.

Michael Veatch <mike.veatch@gordon.edu>
and
Jonathan Senning <jonathan.senning@gordon.edu>

Department of Mathematics and Computer Science, Gordon College

Department of Mathematics and Computer Science, Gordon College