The alternating direction method of multipliers (ADMM) is an algorithm that solves convex optimization problems by breaking them into smaller pieces, each of which are then easier to handle. It has recently found wide application in a number of areas. On this page, we provide a few links to to interesting applications and implementations of the method, along with a few primary references. ADMM is used in a large number of papers at this point, so it is impossible to be comprehensive here. We only intend to highlight a few representative examples in different areas. To keep the listing light, we have only listed more detailed bibliographic information for papers that are not easy to find online; in any case, the information given should be more than enough to track down the papers.
Keywords for this software
References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Fabian Schaipp; Oleg Vlasovets; Christian L. Müller: GGLasso - a Python package for General Graphical Lasso computation (2021) not zbMATH
- Huan, Xun; Safta, Cosmin; Sargsyan, Khachik; Vane, Zachary P.; Lacaze, Guilhem; Oefelein, Joseph C.; Najm, Habib N.: Compressive sensing with cross-validation and stop-sampling for sparse polynomial chaos expansions (2018)
- Giselsson, Pontus; Boyd, Stephen: Linear convergence and metric selection for Douglas-Rachford splitting and ADMM (2017)
- Siegel, Jonathan; Tekin, Omer: Compact support of (L^1) penalized variational problems (2017)