3 papers
math.OC2025
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…
math.OC2024
Derivative-Free Optimization via Adaptive Sampling Strategies
Raghu Bollapragada, Cem Karamanli, Stefan M. Wild
In this paper, we present a novel derivative-free optimization framework for solving unconstrained stochastic optimization problems. Many problems in fields ranging from simulation…
math.OC2023
An Adaptive Sampling Augmented Lagrangian Method for Stochastic Optimization with Deterministic Constraints
Raghu Bollapragada, Cem Karamanli, Brendan Keith +3
The primary goal of this paper is to provide an efficient solution algorithm based on the augmented Lagrangian framework for optimization problems with a stochastic objective funct…