Stochastic Algorithms for Large-Scale Composite Optimization: the Case of Single-Shot X-FEL Imaging

Autor:	Luke, D. Russell, Schultze, Steffen, Grubmüller, Helmut
Rok vydání:	2024
Předmět:	Mathematics - Optimization and Control 65C40 90C26 90C06
Druh dokumentu:	Working Paper
Popis:	We apply a recently developed framework for analyzing the convergence of stochastic algorithms to the general problem of large-scale nonconvex composite optimization more generally, and nonconvex likelihood maximization in particular. Our theory is demonstrated on a stochastic gradient descent algorithm for determining the electron density of a molecule from random samples of its scattering amplitude. Numerical results on an idealized synthetic example provide a proof of concept. This opens the door to a broad range of algorithmic possibilities and provides a basis for evaluating and comparing different strategies. While this case study is very specific, it shares a structure that transfers easily to many problems of current interest, particularly in machine learning. Comment: 20 pages, 10 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.13454 Zobrazit plný text záznamu View this record from Arxiv