5 papers
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…
Modified Line Search Sequential Quadratic Methods for Equality-Constrained Optimization with Unified Global and Local Convergence Guarantees
Albert S. Berahas, Raghu Bollapragada, Jiahao Shi
In this paper, we propose a method that has foundations in the line search sequential quadratic programming paradigm for solving general nonlinear equality constrained optimization…
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…
Adaptive Consensus: A network pruning approach for decentralized optimization
Suhail M. Shah, Albert S. Berahas, Raghu Bollapragada
We consider network-based decentralized optimization problems, where each node in the network possesses a local function and the objective is to collectively attain a consensus sol…
A Stochastic Gradient Tracking Algorithm for Decentralized Optimization With Inexact Communication
Suhail M. Shah, Raghu Bollapragada
Decentralized optimization is typically studied under the assumption of noise-free transmission. However, real-world scenarios often involve the presence of noise due to factors su…