171 citations · 898 across the 46 of their papers we have counts for
4 papers · 2 filters
Taylor approximation for chance constrained optimization problems governed by partial differential equations with high-dimensional random parameters
Peng Chen, Omar Ghattas
We propose a fast and scalable optimization method to solve chance or probabilistic constrained optimization problems governed by partial differential equations (PDEs) with high-di…
Optimal design of acoustic metamaterial cloaks under uncertainty
Peng Chen, Michael R. Haberman, Omar Ghattas
In this work, we consider the problem of optimal design of an acoustic cloak under uncertainty and develop scalable approximation and optimization methods to solve this problem. Th…
Ill-Posedness and Optimization Geometry for Nonlinear Neural Network Training
Thomas O'Leary-Roseberry, Omar Ghattas
In this work we analyze the role nonlinear activation functions play at stationary points of dense neural network training problems. We consider a generic least squares loss functi…
Low Rank Saddle Free Newton: A Scalable Method for Stochastic Nonconvex Optimization
Thomas O'Leary-Roseberry, Nick Alger, Omar Ghattas
In modern deep learning, highly subsampled stochastic approximation (SA) methods are preferred to sample average approximation (SAA) methods because of large data sets as well as g…