Abstract
Matrix completion under interval uncertainty can be cast as a matrix completion problem with element-wise box constraints. We present an efficient alternating-direction parallel coordinate-descent method for the problem. We show that the method outperforms any other known method on a benchmark in image in-painting in terms of signal-to-noise ratio, and that it provides high-quality solutions for an instance of collaborative filtering with 100,198,805 recommendations within 5 minutes on a single personal computer.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 35-43 |
| Number of pages | 9 |
| Journal | European Journal of Operational Research |
| Volume | 256 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2017 |
Bibliographical note
Funding Information:The authors are grateful for the numerous suggestions of the anonymous reviewers as well as the editor that have helped them to improve both the presentation and contents of the paper. In addition, the first author acknowledges funding from the European Union Horizon 2020 Programme (Horizon2020/2014-2020), under grant agreement number 688380 . The second author would like to acknowledge support from the EPSRC Grant EP/K02325X/1 , Accelerated Coordinate Descent Methods for Big Data Optimization.
Publisher Copyright:
© 2016 The Authors
Keywords
- Collaborative filtering
- Coordinate descent
- Large-scale optimization
- Matrix completion
- Robust optimization
ASJC Scopus subject areas
- Computer Science(all)
- Modeling and Simulation
- Management Science and Operations Research
- Information Systems and Management
- Industrial and Manufacturing Engineering