7 papers
A Gradient Sampling Algorithm for Noisy Nonsmooth Nonconvex Optimization
Albert S. Berahas, Frank E. Curtis, Lara Zebiane
An algorithm is proposed, analyzed, and tested for minimizing locally Lipschitz objective functions that may be nonconvex and/or nonsmooth. The algorithm, which is built upon the g…
Negative Curvature Methods with High-Probability Complexity Guarantees for Stochastic Nonconvex Optimization
Albert S. Berahas, Raghu Bollapragada, Wanping Dong
This paper develops negative curvature methods for continuous nonlinear unconstrained optimization in stochastic settings, in which function, gradient, and Hessian information is a…
A line search framework with restarting for noisy optimization problems
Albert S. Berahas, Michael J. O'Neill, Clément W. Royer
Nonlinear optimization methods are typically iterative and make use of gradient information to determine a direction of improvement and function information to effectively check fo…
Retrospective Approximation Sequential Quadratic Programming for Stochastic Optimization with General Deterministic Nonlinear Constraints
Albert S. Berahas, Raghu Bollapragada, Shagun Gupta
In this paper, we propose a framework based on the Retrospective Approximation (RA) paradigm to solve optimization problems with a stochastic objective function and general nonline…
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…
Exploiting Negative Curvature in Conjunction with Adaptive Sampling: Theoretical Results and a Practical Algorithm
Albert S. Berahas, Raghu Bollapragada, Wanping Dong
In this paper, we propose algorithms that exploit negative curvature for solving noisy nonlinear nonconvex unconstrained optimization problems. We consider both deterministic and s…