Distributed Matrix Completion: Application to Cooperative Positioning in Noisy Environments

Abstract

The PI and collaborators developed novel algorithms for positioning, building on earlier developments in matrix completion and high-dimensional statistics. In particular, a distributed version of matrix completion-based positioning, and a gossip version of low-rank approximation were developed. A convex relaxation for positioning in the presence of noise was shown to be constant-optimal. Additional contributions were made in several other areas: Finding dense substructures of large networks in nearly linear time; Approximate message passing algorithms and in particular their application to spatially-coupled compressed sensing; Measures of statistical significance in high dimension.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 11, 2013
Accession Number
ADA595375

Entities

People

  • Andrea Montanari

Organizations

  • Stanford University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Compressed Sensing
  • Computational Complexity
  • Computer Science
  • Data Sets
  • Electrical Engineering
  • Information Theory
  • Linear Algebra
  • Phase
  • Phase Transformations
  • Probability
  • Probability Distributions
  • Signal Processing
  • Statistics
  • Theoretical Computer Science
  • Transitions

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Networking
  • Linear Algebra