Explore methods for $\ell_p$-$\ell_q$ minimization and their applications in image restoration and regression with nonconvex loss and penalty in this one-hour virtual seminar. Delve into minimization problems with objective functions combining fidelity and regularization terms determined by p-norms and q-norms, respectively, where 0
Methods for L_p-L_q Minimization in Image Restoration and Regression - SIAM-IS Seminar
Society for Industrial and Applied Mathematics via YouTube
Overview
Syllabus
Introduction
Overview
Problem
lasso method
norms
nonnegative pixels
Outline
Starting Point
Smooth Function
Adaptive Measurement
Convergence
Example
Crossvalidation
Sparse Representation
Tensor Products
Modulus iterative method
Relative error
Applications
Questions
Taught by
Society for Industrial and Applied Mathematics
