4 citations · 12 across the 10 of their papers we have counts for
10 papers
An Adaptive Augmented Lagrangian Method for Deterministic and Stochastic Nonconvex Optimization
Tianzhu Liu, Michael J. O'Neill
We present an inexact Augmented Lagrangian algorithm for solving nonlinear, non-convex optimization problems. Unlike most recently proposed Augmented Lagrangian methods with worst-…
Complexity of an inexact stochastic SQP algorithm for equality constrained optimization
Michael J. O'Neill, Aoji Tang
In this paper, we consider nonlinear optimization problems with a stochastic objective function and deterministic equality constraints. We propose an inexact two-stepsize stochasti…
A Sequential Cubic Programming Method with Second-Order Complexity Guarantees for Equality Constrained Optimization
Nikos Dimou, Michael J. O'Neill
We develop a new method for equality constrained optimization problems based on a sequential cubic programming framework. Each iteration utilizes a step decomposition based on the…
Worst-Case Complexity of an SQP Method for Nonlinear Equality Constrained Stochastic Optimization
Frank E. Curtis, Michael J. O'Neill, Daniel P. Robinson
A worst-case complexity bound is proved for a sequential quadratic optimization (commonly known as SQP) algorithm that has been designed for solving optimization problems involving…
A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear Equality Constrained Optimization with Rank-Deficient Jacobians
Albert S. Berahas, Frank E. Curtis, Michael J. O'Neill +1
A sequential quadratic optimization algorithm is proposed for solving smooth nonlinear equality constrained optimization problems in which the objective function is defined by an e…
Optimizing the Parameters of A Physical Exercise Dose-Response Model: An Algorithmic Comparison
Mark Connor, Michael O'Neill
The purpose of this research was to compare the robustness and performance of a local and global optimization algorithm when given the task of fitting the parameters of a common no…
A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees
Michael O'Neill, Stephen J. Wright
We describe a line-search algorithm which achieves the best-known worst-case complexity results for problems with a certain "strict saddle" property that has been observed to hold…
A Log-Barrier Newton-CG Method for Bound Constrained Optimization with Complexity Guarantees
Michael O'Neill, Stephen J. Wright
We describe an algorithm based on a logarithmic barrier function, Newton's method, and linear conjugate gradients that obtains an approximate minimizer of a smooth function over th…
A Newton-CG Algorithm with Complexity Guarantees for Smooth Unconstrained Optimization
Clément W. Royer, Michael O'Neill, Stephen J. Wright
We consider minimization of a smooth nonconvex objective function using an iterative algorithm based on Newton's method and the linear conjugate gradient algorithm, with explicit d…
Behavior of Accelerated Gradient Methods Near Critical Points of Nonconvex Functions
Michael O'Neill, Stephen J. Wright
We examine the behavior of accelerated gradient methods in smooth nonconvex unconstrained optimization, focusing in particular on their behavior near strict saddle points. Accelera…