  Iterative minimization algorithms appear in various areas including machine
learning, neural networks, and information theory.The em algorithm is one of
the famous iterative minimization algorithms in the area of machine learning,
and the Arimoto-Blahut algorithm is a typical iterative algorithm in the area
of information theory.However, these two topics had been separately studied for
a long time. In this paper, we generalize an algorithm that was recently
proposed in the context of the Arimoto-Blahut algorithm.Then, we show various
convergence theorems, one of which covers the case when each iterative step is
done approximately.Also, we apply this algorithm to the target problem of the
em algorithm, and propose its improvement. In addition, we apply it to other
various problems in information theory.
