activity
20182022
most citedWorst-Case Complexity of an SQP Method for Nonlinear Equality Constrained Stochastic Optimization

4 citations · 7 across the 3 of their papers we have counts for

collaborators

5 papers

math.OC20224 cited

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…

cs.NE20201 cited

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…

math.OC20202 cited

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…

math.OC2019

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…

math.OC2018

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…