58 citations · 99 across the 23 of their papers we have counts for
29 papers
Stochastic Augmented Lagrangian Framework with Second-Order Convergence Guarantees for Nonconvex Expectation-Constrained Optimization
Raghu Bollapragada, Yash Kumar
In this paper, we propose and analyze an augmented Lagrangian framework for solving stochastic nonconvex optimization problems with expectation-based equality constraints over a cl…
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…
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…
A Flexible Gradient Tracking Algorithmic Framework for Decentralized Optimization
Albert S. Berahas, Raghu Bollapragada, Shagun Gupta
In decentralized optimization over networks, each node in the network has a portion of the global objective function and the aim is to collectively optimize this function. Gradient…
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…
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…
Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized Optimization
Albert S. Berahas, Raghu Bollapragada, Shagun Gupta
Gradient tracking methods have emerged as one of the most popular approaches for solving decentralized optimization problems over networks. In this setting, each node in the networ…
On the fast convergence of minibatch heavy ball momentum
Raghu Bollapragada, Tyler Chen, Rachel Ward
Simple stochastic momentum methods are widely used in machine learning optimization, but their good practical performance is at odds with an absence of theoretical guarantees of ac…
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…
Scalable Unidirectional Pareto Optimality for Multi-Task Learning with Constraints
Soumyajit Gupta, Gurpreet Singh, Raghu Bollapragada +1
Multi-objective optimization (MOO) problems require balancing competing objectives, often under constraints. The Pareto optimal solution set defines all possible optimal trade-offs…
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization
Raghu Bollapragada, Stefan M. Wild
We consider unconstrained stochastic optimization problems with no available gradient information. Such problems arise in settings from derivative-free simulation optimization to r…
A Retrospective Approximation Approach for Smooth Stochastic Optimization
David Newton, Raghu Bollapragada, Raghu Pasupathy +1
Stochastic Gradient (SG) is the defacto iterative technique to solve stochastic optimization (SO) problems with a smooth (non-convex) objective and a stochastic first-order ora…
Constrained and Composite Optimization via Adaptive Sampling Methods
Yuchen Xie, Raghu Bollapragada, Richard Byrd +1
The motivation for this paper stems from the desire to develop an adaptive sampling method for solving constrained optimization problems in which the objective function is stochast…
Optimization and Supervised Machine Learning Methods for Fitting Numerical Physics Models without Derivatives
Raghu Bollapragada, Matt Menickelly, Witold Nazarewicz +3
We address the calibration of a computationally expensive nuclear physics model for which derivative information with respect to the fit parameters is not readily available. Of par…
On the Convergence of Nested Decentralized Gradient Methods with Multiple Consensus and Gradient Steps
Albert S. Berahas, Raghu Bollapragada, Ermin Wei
In this paper, we consider minimizing a sum of local convex objective functions in a distributed setting, where the cost of communication and/or computation can be expensive. We ex…
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization
Raghu Bollapragada, Stefan M. Wild
We consider stochastic zero-order optimization problems, which arise in settings from simulation optimization to reinforcement learning. We propose an adaptive sampling quasi-Newto…
Nonlinear Acceleration of Momentum and Primal-Dual Algorithms
Raghu Bollapragada, Damien Scieur, Alexandre d'Aspremont
We describe convergence acceleration schemes for multistep optimization algorithms. The extrapolated solution is written as a nonlinear average of the iterates produced by the orig…