Sensitivity analysis in optimization theory explores how the solution to a particular optimization problem changes as the objective function or constraints ...
More specifically, we show that in a noiseless and RIP-less setting [11], the recovery process of a compressed sensing framework is a binary event in the sense ...
This paper shows that in a noiseless and RIP-less setting, the recovery process of a compressed sensing framework is a binary event in the sense that either ...
Abstract— Sensitivity analysis in optimization theory explores how the solution to a particular optimization problem changes.
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The objective of this paper is to analyze the sensitivity of compressive sensing solutions to perturbations (inaccuracies) in matrix A and measurement y, i.e., ...
In this paper, we are interested in compressive sensing solutions under a general form of measurement y = (A + B)x + v in which B and v describe modeling and ...
In this theory, the sensing mechanism simply selects sensing vectors independently at random from a probabil- ity distribution F; it includes all standard ...
There is a great number of methods of modal analysis in the published literature for estimating the parameters (θ, φ), and their performance is well understood.
Missing: RIPless | Show results with:RIPless
Sensitivity analysis in RIPless compressed sensing. , 2014, , . 1. 54. Efficient compressed sensing SENSE parallel MRI reconstruction with joint sparsity ...
In this work, we introduce a general framework to minimize the sensitivity of compressed sensing and low rank matrix recovery schemes to inter frame motion.