4 papers
Gradient-Free Optimization for Matrix functions
Sawyer Allen, Cash Cherry, Aidan Eck +2
We consider the task of optimizing smooth, possibly non-convex functions of a matrix variable given access only to directional derivatives rather than full gradients. This setting…
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…
Applications of Automatic Differentiation in Image Registration
Warin Watson, Cash Cherry, Rachelle Lang
We demonstrate that automatic differentiation (AD), which has become commonly available in machine learning frameworks, is an efficient way to explore ideas that lead to algorithmi…
Rigid-Recurrent Sequences for Actions of Finite Exponent Groups
Cash Cherry
The focus of this paper is to better understand the coexistence of rigidity, weak mixing, and recurrence by constructing thin sets in the product of countably many copies of the fi…