Applications of GAMP

This lecture explores the application of the Generalized Approximate Message Passing (GAMP) algorithm to the lasso problem, demonstrating how to simplify the AMP version for lasso compression. The instructor discusses the choice of functions, the denoiser, and the proximal operator, showing how to map the recursion and analyze the state evolution of the algorithm. The lecture delves into the performance comparison of different algorithms, the implications of the fixed points, and the challenges posed by hard phases in optimization problems. It concludes with a detailed examination of the Kubili machine and the specialization phenomenon in neural networks.