S
Stephen Becker
Researcher at University of Colorado Boulder
Publications - 117
Citations - 5571
Stephen Becker is an academic researcher from University of Colorado Boulder. The author has contributed to research in topics: Convex optimization & Rank (linear algebra). The author has an hindex of 25, co-authored 115 publications receiving 4931 citations. Previous affiliations of Stephen Becker include Wesleyan University & University of Lorraine.
Papers
More filters
Journal ArticleDOI
Quantum state tomography via compressed sensing.
TL;DR: These methods are specialized for quantum states that are fairly pure, and they offer a significant performance improvement on large quantum systems, and are able to reconstruct an unknown density matrix of dimension d and rank r using O(rdlog²d) measurement settings, compared to standard methods that require d² settings.
Journal ArticleDOI
NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
TL;DR: A smoothing technique and an accelerated first-order algorithm are applied and it is demonstrated that this approach is ideally suited for solving large-scale compressed sensing reconstruction problems and is robust in the sense that its excellent performance across a wide range of problems does not depend on the fine tuning of several parameters.
Journal ArticleDOI
Templates for convex cone problems with applications to sparse signal recovery
TL;DR: A general framework for solving a variety of convex cone problems that frequently arise in signal processing, machine learning, statistics, and other fields, and results showing that the smooth and unsmoothed problems are sometimes formally equivalent are applied.
Journal ArticleDOI
Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics
TL;DR: A recent review of convex optimization algorithms for big data can be found in this article, which aim to reduce the computational, storage, and communications bottlenecks of big data.
Journal ArticleDOI
Fractional Stokes-Einstein and Debye-Stokes-Einstein relations in a network-forming liquid.
TL;DR: It is found that the emergence of fractional SE and DSE relations at low temperature is ubiquitous in this system, with exponents that vary little over a range of distinct physical regimes.