2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDSPTM-3.2
Paper Title A DECENTRALIZED VARIANCE-REDUCED METHOD FOR STOCHASTIC OPTIMIZATION OVER DIRECTED GRAPHS
Authors Muhammad Qureshi, Tufts University, United States; Ran Xin, Soummya Kar, Carnegie Mellon University, United States; Usman Khan, Tufts University, United States
SessionSPTM-3: Estimation, Detection and Learning over Networks 1
LocationGather.Town
Session Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Poster
Topic Signal Processing Theory and Methods: [OPT] Optimization Methods for Signal Processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In this paper, we propose a decentralized first-order stochastic optimization method PushSAGA for finite-sum minimization over a strongly connected directed graph. This method features local variance reduction to remove the uncertainty caused by random sampling of the local gradients, global gradient tracking to address the distributed nature of the data, and push-sum consensus to handle the imbalance caused by the directed nature of the underlying graph. We show that, for a sufficiently small step-size, PushSAGA linearly converges to the optimal solution for smooth and strongly convex problems, making it the first linearly-convergent stochastic algorithm over arbitrary strongly-connected directed graphs. We illustrate the behavior and convergence properties of PushSAGA with the help of numerical experiments for strongly convex and nonconvex problems.