Statistical Consulting Seminars
Speaker：Prof. Hua Zhou (Department of Biostatistics, University of California, Los Angeles)
National Yang Ming Chiao Tung University
Institute of Statistics
Statistical Consulting Seminars
Topic：Recent Advances in MM Optimization Algorithms
Speaker：Prof. Hua Zhou (Department of Biostatistics, University of California, Los Angeles)
Time：Dec 19 (Friday) , 2025, 14:20-15:10
Place： 4F-427, Assembly Building I
Abstract
The majorization-minimization (MM) principle is an extremely general framework for deriving optimization algorithms. It includes the expectation-maximization (EM) algorithm, proximal gradient algorithm, concave-convex procedure, quadratic lower bound algorithm, and proximal distance algorithm as special cases. Besides numerous applications in statistics, optimization, and imaging, the MM principle finds wide applications in large-scale machine learning problems such as matrix completion, discriminant analysis, and nonnegative matrix factorizations. This talk presents some novel applications of the MM principle in the big data setting, including parallel block least squares, deweighting weighted least squares, large-scale variance component model, independent component analysis, and multi-level Monte Carlo.