3 citations · 3 across the 4 of their papers we have counts for
6 papers
The dual approach to non-negative super-resolution: perturbation analysis and data fidelity
Stéphane Chrétien, Andrew Thompson, Bogdan Toader
We study the problem of super-resolution, where we recover the locations and weights of non-negative point sources from (potentially noisy) samples of their convolution with a Gaus…
The dual approach to non-negative super-resolution: impact on primal reconstruction accuracy
Stephane Chretien, Andrew Thompson, Bogdan Toader
We study the problem of super-resolution, where we recover the locations and weights of non-negative point sources from a few samples of their convolution with a Gaussian kernel. I…
Sparse Inverse Problems Over Measures: Equivalence of the Conditional Gradient and Exchange Methods
Armin Eftekhari, Andrew Thompson
We study an optimization program over nonnegative Borel measures that encourages sparsity in its solution. Efficient solvers for this program are in increasing demand, as it arises…
Sparse non-negative super-resolution -- simplified and stabilised
Armin Eftekhari, Jared Tanner, Andrew Thompson +2
The convolution of a discrete measure, , with a local window function, , is a common model for a measurement device whose resolution is substantia…
Spectral Clustering: An empirical study of Approximation Algorithms and its Application to the Attrition Problem
B. Cung, T. Jin, J. Ramirez +3
Clustering is the problem of separating a set of objects into groups (called clusters) so that objects within the same cluster are more similar to each other than to those in diffe…
Phase Transitions for Greedy Sparse Approximation Algorithms
Jeffrey D. Blanchard, Coralia Cartis, Jared Tanner +1
A major enterprise in compressed sensing and sparse approximation is the design and analysis of computationally tractable algorithms for recovering sparse, exact or approximate, so…