32 citations · 37 across the 7 of their papers we have counts for
7 papers
GeNIOS: an (almost) second-order operator-splitting solver for large-scale convex optimization
Theo Diamandis, Zachary Frangella, Shipu Zhao +2
We introduce the GEneralized Newton Inexact Operator Splitting solver (GeNIOS) for large-scale convex optimization. GeNIOS speeds up ADMM by approximately solving approximate subpr…
PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates
Zachary Frangella, Pratik Rathore, Shipu Zhao +1
This paper introduces PROMISE (econditioned Stochastic ptimization ethods by ncorporating calable Curvature $\textbf{E}…
On the (linear) convergence of Generalized Newton Inexact ADMM
Zachary Frangella, Theo Diamandis, Bartolomeo Stellato +1
This paper presents GeNI-ADMM, a framework for large-scale composite convex optimization that facilitates theoretical analysis of both existing and new approximate ADMM schemes. Ge…
SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates
Zachary Frangella, Pratik Rathore, Shipu Zhao +1
SketchySGD improves upon existing stochastic gradient methods in machine learning by using randomized low-rank approximations to the subsampled Hessian and by introducing an automa…
NysADMM: faster composite convex optimization via low-rank approximation
Shipu Zhao, Zachary Frangella, Madeleine Udell
This paper develops a scalable new algorithm, called NysADMM, to minimize a smooth convex loss function with a convex regularizer. NysADMM accelerates the inexact Alternating Direc…
An automatic system to detect equivalence between iterative algorithms
Shipu Zhao, Laurent Lessard, Madeleine Udell
When are two algorithms the same? How can we be sure a recently proposed algorithm is novel, and not a minor twist on an existing method? In this paper, we present a framework for…
Distributionally Robust Chance Constrained Programming with Generative Adversarial Networks (GANs)
Shipu Zhao, Fengqi You
This paper presents a novel deep learning based data-driven optimization method. A novel generative adversarial network (GAN) based data-driven distributionally robust chance const…