8 papers · 1 filter
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…
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…
On the Convergence and Complexity of the Stochastic Central Finite-Difference Based Gradient Estimation Methods
Raghu Bollapragada, Cem Karamanli
This paper presents an algorithmic framework for solving unconstrained stochastic optimization problems using only stochastic function evaluations. We employ central finite-differe…
Efficient Mathematical Programming Formulation and Algorithmic Framework for Optimal Camera Placement
Yash Kumar, Raghu Bollapragada, Benjamin D. Leibowicz
Optimal camera placement plays a crucial role in applications such as surveillance, environmental monitoring, and infrastructure inspection. Even highly abstracted versions of this…
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…
Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods
Thomas O'Leary-Roseberry, Raghu Bollapragada
We consider minimizing finite-sum and expectation objective functions via Hessian-averaging based subsampled Newton methods. These methods allow for gradient inexactness and have f…