Scalable signal reconstruction for a broad range of applications
Abstract
Signal reconstruction problem (SRP) is an important optimization problem where the objective is to identify a solution to an underdetermined system of linear equations that is closest to a given prior. It has a substantial number of applications in diverse areas, such as network traffic engineering, medical image reconstruction, acoustics, astronomy, and many more. Unfortunately, most of the common approaches for solving SRP do not scale to large problem sizes. We propose a novel and scalable algorithm for solving this critical problem. Specifically, we make four major contributions. First, we propose a dual formulation of the problem and develop the DIRECT algorithm that is significantly more efficient than the state of the art. Second, we show how adapting database techniques developed for scalable similarity joins provides a substantial speedup over DIRECT. Third, we describe several practical techniques that allow our algorithm to scale---on a single machine---to settings that are orders of magnitude larger than previously studied. Finally, we use the database techniques of materialization and reuse to extend our result to dynamic settings where the input to the SRP changes. Extensive experiments on real-world and synthetic data confirm the efficiency, effectiveness, and scalability of our proposal.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 25, 2021
- Source ID
- 10.1145/3441689
Entities
People
- Abolfazl Asudeh
- Azade Nazi
- Divesh Srivastava
- Gautam Das
- Jees Augustine
- Nan Zhang
- Saravanan Thirumuruganathan
Organizations
- AT&T
- American University
- Army Research Office
- Google Brain
- National Science Foundation
- Qatar Computing Research Institute
- University of Illinois at Chicago
- University of Texas at Arlington