51 citations · 89 across the 3 of their papers we have counts for
8 papers
Concentration of the Intrinsic Volumes of a Convex Body
Martin Lotz, Michael B. McCoy, Ivan Nourdin +2
The intrinsic volumes are measures of the content of a convex body. This paper uses probabilistic and information-theoretic methods to study the sequence of intrinsic volumes of a…
Convexity in source separation: Models, geometry, and algorithms
Michael B. McCoy, Volkan Cevher, Quoc Tran Dinh +2
Source separation or demixing is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separat…
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…
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…
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…