120 citations · 245 across the 34 of their papers we have counts for
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math.OC2020
An adaptive stochastic gradient-free approach for high-dimensional blackbox optimization
Anton Dereventsov, Clayton G. Webster, Joseph D. Daws
In this work, we propose a novel adaptive stochastic gradient-free (ASGF) approach for solving high-dimensional nonconvex optimization problems based on function evaluations. We em…
math.OC2017★ 3 cited
On the strong convergence of forward-backward splitting in reconstructing jointly sparse signals
Nick Dexter, Hoang Tran, Clayton Webster
We consider the problem of reconstructing an infinite set of sparse, finite-dimensional vectors, that share a common sparsity pattern, from incomplete measurements. This is in cont…
math.OC2017
A class of null space conditions for sparse recovery via nonconvex, non-separable minimizations
Hoang Tran, Clayton Webster
For the problem of sparse recovery, it is widely accepted that nonconvex minimizations are better than penalty in enhancing the sparsity of solution. However, to date, the…