25 citations · 77 across the 32 of their papers we have counts for
4 papers · 1 filter
Gradient Descent in the Absence of Global Lipschitz Continuity of the Gradients
Vivak Patel, Albert S. Berahas
Gradient descent (GD) is a collection of continuous optimization methods that have achieved immeasurable success in practice. Owing to data science applications, GD with diminishin…
An Adaptive Sampling Sequential Quadratic Programming Method for Equality Constrained Stochastic Optimization
Albert S. Berahas, Raghu Bollapragada, Baoyu Zhou
This paper presents a methodology for using varying sample sizes in sequential quadratic programming (SQP) methods for solving equality constrained stochastic optimization problems…
First- and Second-Order High Probability Complexity Bounds for Trust-Region Methods with Noisy Oracles
Liyuan Cao, Albert S. Berahas, Katya Scheinberg
In this paper, we present convergence guarantees for a modified trust-region method designed for minimizing objective functions whose value and gradient and Hessian estimates are c…
Accelerating Stochastic Sequential Quadratic Programming for Equality Constrained Optimization using Predictive Variance Reduction
Albert S. Berahas, Jiahao Shi, Zihong Yi +1
In this paper, we propose a stochastic method for solving equality constrained optimization problems that utilizes predictive variance reduction. Specifically, we develop a method…