120 citations · 154 across the 17 of their papers we have counts for
7 papers · 1 filter
Analysis of The Ratio of and Norms in Compressed Sensing
Yiming Xu, Akil Narayan, Hoang Tran +1
We first propose a novel criterion that guarantees that an -sparse signal is the local minimizer of the objective; our criterion is interpretable and useful in p…
Reconstructing high-dimensional Hilbert-valued functions via compressed sensing
Nick Dexter, Hoang Tran, Clayton Webster
We present and analyze a novel sparse polynomial technique for approximating high-dimensional Hilbert-valued functions, with application to parameterized partial differential equat…
A mixed regularization approach for sparse simultaneous approximation of parameterized PDEs
Nick Dexter, Hoang Tran, Clayton Webster
We present and analyze a novel sparse polynomial technique for the simultaneous approximation of parameterized partial differential equations (PDEs) with deterministic and stochast…
Reconstruction of jointly sparse vectors via manifold optimization
Armenak Petrosyan, Hoang Tran, Clayton Webster
In this paper, we consider the challenge of reconstructing jointly sparse vectors from linear measurements. Firstly, we show that by utilizing the rank of the output data matrix we…
Analysis of sparse recovery for Legendre expansions using envelope bound
Hoang Tran, Clayton Webster
We provide novel sufficient conditions for the uniform recovery of sparse Legendre expansions using minimization, where the sampling points are drawn according to orthogon…
Polynomial approximation via compressed sensing of high-dimensional functions on lower sets
Abdellah Chkifa, Nick Dexter, Hoang Tran +1
This work proposes and analyzes a compressed sensing approach to polynomial approximation of complex-valued functions in high dimensions. Of particular interest is the setting wher…
A sparse grid method for Bayesian uncertainty quantification with application to large eddy simulation turbulence models
Hoang A. Tran, Clayton G. Webster, Guannan Zhang
There is wide agreement that the accuracy of turbulence models suffer from their sensitivity with respect to physical input data, the uncertainties of user-elected parameters, as w…