UTRC CDS Seminar: Rachael Tappenden, "Flexible ADMM for Big Data Applications"

Friday, November 20, 2015
11:00 a.m.
2460 AVW
Regina King
301 405 6576
rking12@umd.edu

Flexible ADMM for Big Data Applications

Rachael Tappenden
School of Applied Math and Statistics
Johns Hopkins University

Abstract
In this talk we present a flexible Alternating Direction Method of Multipliers (F-ADMM) algorithm for solving optimization problems involving a strongly convex objective function that is separable into n blocks, subject to linear equality constraints. The F-ADMM algorithm updates the blocks of variables in a Gauss-Seidel fashion, and the subproblems arising within F-ADMM include a regularization term so that they can be solved efficiently. The algorithm is globally convergent. We also introduce a hybrid variant called H-ADMM that is partially parallelizable, which is important in a big data setting. Convergence of H-ADMM follows directly from the convergence properties of F-ADMM. We present numerical experiments to demonstrate the practical performance of this algorithm.

Audience: Graduate  Undergraduate  Faculty  Post-Docs  Alumni 

 

October 2019

SU MO TU WE TH FR SA
29 30 1 2 3 4 5
6 7 8 9 10 11 12
13 14 15 16 17 18 19
20 21 22 23 24 25 26
27 28 29 30 31 1 2
Submit an Event