Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization

Understand

Majorization-minimization algorithms consist of iteratively minimizing a majorizing surrogate of an objective function.

• Because of its simplicity and its wide applicability, this principle has been very popular in statistics and in signal processing.
• In this paper, we intend to make this principle scalable.
• We introduce a stochastic majorization-minimization scheme which is able to deal with large-scale or possibly infinite data sets.