Iterative Algorithms

The present chapter surveys computational algorithms for solving the independent component analysis (ICA) problem. Most of these algorithms rely on gradient or Newton iterations for contrast function maximization, and can work either in batch or adaptive processing mode. After briefly summarizing the common tools employed in their design and analysis, the chapter reviews a variety of iterative techniques ranging from pioneering neural network approaches and relative (or natural) gradient methods to Newton-like fixed-point algorithms as well as methods based on some form of optimal step-size coefficient.