16 citations · 56 across the 12 of their papers we have counts for
14 papers
A Gauss-Newton Method with No Additional PDE Solves Beyond Gradient Evaluation for Large-Scale PDE-Constrained Inverse Problems
Cash Cherry, Samy Wu Fung, Luis Tenorio +1
Partial Differential Equation (PDE)-constrained optimization problems often take the form of an optimization of an objective function given as a sum of loss terms. Each function or…
Estimating a potential without the agony of the partition function
Eldad Haber, Moshe Eliasof, Luis Tenorio
Estimating a Gibbs density function given a sample is an important problem in computational statistics and statistical learning. Although the well established maximum likelihood me…
Zeroth order optimization with orthogonal random directions
David Kozak, Cesare Molinari, Lorenzo Rosasco +2
We propose and analyze a randomized zeroth-order approach based on approximating the exact gradient byfinite differences computed in a set of orthogonal random directions that chan…
A stochastic subspace approach to gradient-free optimization in high dimensions
David Kozak, Stephen Becker, Alireza Doostan +1
We present a stochastic descent algorithm for unconstrained optimization that is particularly efficient when the objective function is slow to evaluate and gradients are not easily…
Sampled Limited Memory Methods for Massive Linear Inverse Problems
Julianne Chung, Matthias Chung, J. Tanner Slagel +1
In many modern imaging applications the desire to reconstruct high resolution images, coupled with the abundance of data from acquisition using ultra-fast detectors, have led to ne…
Stochastic Subspace Descent
David Kozak, Stephen Becker, Alireza Doostan +1
We present two stochastic descent algorithms that apply to unconstrained optimization and are particularly efficient when the objective function is slow to evaluate and gradients a…