Algorithmic Design of Majorizers for Large-Scale Inverse Problems

Iterative majorize-minimize (MM) (also called optimization transfer) algorithms solve challenging numerical optimization problems by solving a series of "easier" optimization problems that are constructed to guarantee monotonic descent of the cost function. Many MM algorithms replace a computationally expensive Hessian matrix with another more computationally convenient majorizing matrix. These majorizing matrices are often generated using various matrix inequalities, and consequently the set of available majorizers is limited to structures for which these matrix inequalities can be efficiently applied. In this paper, we present a technique to algorithmically design matrix majorizers with wide varieties of structures. We use a novel duality-based approach to avoid the high computational and memory costs of standard semidefinite programming techniques. We present some preliminary results for 2D X-ray CT reconstruction that indicate these more exotic regularizers may significantly accelerate MM algorithms.

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