1.1k citations · 3.9k across the 74 of their papers we have counts for
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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…
From Steiner Formulas for Cones to Concentration of Intrinsic Volumes
Michael B. McCoy, Joel A. Tropp
The intrinsic volumes of a convex cone are geometric functionals that return basic structural information about the cone. Recent research has demonstrated that conic intrinsic volu…
Deriving Matrix Concentration Inequalities from Kernel Couplings
Daniel Paulin, Lester Mackey, Joel A. Tropp
This paper derives exponential tail bounds and polynomial moment inequalities for the spectral norm deviation of a random matrix from its mean value. The argument depends on a matr…
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…
Model-based scaling of the streamwise energy density in high-Reynolds number turbulent channels
Rashad Moarref, Ati S. Sharma, Joel A. Tropp +1
We study the Reynolds number scaling and the geometric self-similarity of a gain-based, low-rank approximation to turbulent channel flows, determined by the resolvent formulation o…