Efficient approximation of discrete memoryless channel capacities

We propose an iterative method for efficiently approximating the capacity of discrete memoryless channels, possibly having additional constraints on the input distribution. Based on duality of convex programming, we derive explicit upper and lower bounds for the capacity. To find an ε-approximation of the capacity, in case of no additional input constraints, the presented method has a computational complexity O(1 over εM²N√(logN)), where N and M denote the input and output alphabet size, and a single iteration has a complexity O(MN).