1.1k citations · 3.9k across the 74 of their papers we have counts for
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Convex recovery of a structured signal from independent random linear measurements
Joel A. Tropp
This chapter develops a theoretical analysis of the convex programming method for recovering a structured signal from independent random linear measurements. This technique deliver…
The achievable performance of convex demixing
Michael B. McCoy, Joel A. Tropp
Demixing is the problem of identifying multiple structured signals from a superimposed, undersampled, and noisy observation. This work analyzes a general framework, based on convex…
Subadditivity of Matrix phi-Entropy and Concentration of Random Matrices
Joel A. Tropp, Richard Y. Chen
Matrix concentration inequalities provide a direct way to bound the typical spectral norm of a random matrix. The methods for establishing these results often parallel classical ar…
Living on the edge: Phase transitions in convex programs with random data
Dennis Amelunxen, Martin Lotz, Michael B. McCoy +1
Recent research indicates that many convex optimization problems with random constraints exhibit a phase transition as the number of constraints increases. For example, this phenom…
Sharp recovery bounds for convex demixing, with applications
Michael B. McCoy, Joel A. Tropp
Demixing refers to the challenge of identifying two structured signals given only the sum of the two signals and prior information about their structures. Examples include the prob…
Robust computation of linear models by convex relaxation
Gilad Lerman, Michael McCoy, Joel A. Tropp +1
Consider a dataset of vector-valued observations that consists of noisy inliers, which are explained well by a low-dimensional subspace, along with some number of outliers. This wo…