6 citations · 22 across the 17 of their papers we have counts for
17 papers
On the Complexity of BFGS Method for Smooth Convex Optimization
Lijun Ding, Jinwen Yang, Baoyu Zhou
We study the BFGS method with an Armijo-Wolfe line search for minimizing convex functions with Lipschitz-continuous gradients, without assuming strong convexity. We establish a glo…
Optimal Two-Step Stepsize Schedule for Stochastic Gradient Methods
Luwei Bai, Baoyu Zhou
Structured nonconstant large stepsizes can improve the convergence of gradient descent in the deterministic setting. However, in stochastic optimization, aggressive stepsizes can a…
Generalization of Silver Stepsize Schedule to Stochastic Optimization
Luwei Bai, Yang Zeng, Baoyu Zhou
This work introduces a two-step stepsize schedule for stochastic gradient methods minimizing smooth strongly convex functions. We consider the setting where only stochastic gradien…
Optimistic Noise-Aware Sequential Quadratic Programming for Equality Constrained Optimization with Rank-Deficient Jacobians
Albert S. Berahas, Jiahao Shi, Baoyu Zhou
We propose and analyze a sequential quadratic programming algorithm for minimizing a noisy nonlinear smooth function subject to noisy nonlinear smooth equality constraints. The alg…
Sequential Quadratic Optimization for Solving Expectation Equality Constrained Stochastic Optimization Problems
Haoming Shen, Yang Zeng, Baoyu Zhou
A sequential quadratic programming method is designed for solving general smooth nonlinear stochastic optimization problems subject to expectation equality constraints. We consider…
On the Convergence of L-shaped Algorithms for Two-Stage Stochastic Programming
John R. Birge, Haihao Lu, Baoyu Zhou
In this paper, we design, analyze, and implement a variant of the two-loop L-shaped algorithms for solving two-stage stochastic programming problems that arise from important appli…
Online Ad Procurement in Non-stationary Autobidding Worlds
Jason Cheuk Nam Liang, Haihao Lu, Baoyu Zhou
Today's online advertisers procure digital ad impressions through interacting with autobidding platforms: advertisers convey high level procurement goals via setting levers such as…
Sequential Quadratic Optimization for Stochastic Optimization with Deterministic Nonlinear Inequality and Equality Constraints
Frank E. Curtis, Daniel P. Robinson, Baoyu Zhou
A sequential quadratic optimization algorithm for minimizing an objective function defined by an expectation subject to nonlinear inequality and equality constraints is proposed, a…
A Sequential Quadratic Programming Method with High Probability Complexity Bounds for Nonlinear Equality Constrained Stochastic Optimization
Albert S. Berahas, Miaolan Xie, Baoyu Zhou
A step-search sequential quadratic programming method is proposed for solving nonlinear equality constrained stochastic optimization problems. It is assumed that constraint functio…
Grad-GradaGrad? A Non-Monotone Adaptive Stochastic Gradient Method
Aaron Defazio, Baoyu Zhou, Lin Xiao
The classical AdaGrad method adapts the learning rate by dividing by the square root of a sum of squared gradients. Because this sum on the denominator is increasing, the method ca…
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…
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…
Inexact Sequential Quadratic Optimization for Minimizing a Stochastic Objective Function Subject to Deterministic Nonlinear Equality Constraints
Frank E. Curtis, Daniel P. Robinson, Baoyu Zhou
An algorithm is proposed, analyzed, and tested experimentally for solving stochastic optimization problems in which the decision variables are constrained to satisfy equations defi…
Manifold Sampling for Optimizing Nonsmooth Nonconvex Compositions
Jeffrey Larson, Matt Menickelly, Baoyu Zhou
We propose a manifold sampling algorithm for minimizing a nonsmooth composition , where we assume is nonsmooth and may be inexpensively computed in closed form and…
Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization
Albert Berahas, Frank E. Curtis, Daniel P. Robinson +1
Sequential quadratic optimization algorithms are proposed for solving smooth nonlinear optimization problems with equality constraints. The main focus is an algorithm proposed for…
Limited-Memory BFGS with Displacement Aggregation
Albert S. Berahas, Frank E. Curtis, Baoyu Zhou
A displacement aggregation strategy is proposed for the curvature pairs stored in a limited-memory BFGS (a.k.a. L-BFGS) method such that the resulting (inverse) Hessian approximati…
A Self-Correcting Variable-Metric Algorithm Framework for Nonsmooth Optimization
Frank E. Curtis, Daniel P. Robinson, Baoyu Zhou
An algorithm framework is proposed for minimizing nonsmooth functions. The framework is variable-metric in that, in each iteration, a step is computed using a symmetric positive de…