activity
20152022
most citedSequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization

6 citations · 21 across the 11 of their papers we have counts for

collaborators
Showing math.OCShow all

12 papers · 1 filter

math.OC2022

Inexact Proximal-Gradient Methods with Support Identification

Yutong Dai, Daniel P. Robinson

We consider the proximal-gradient method for minimizing an objective function that is the sum of a smooth function and a non-smooth convex function. A feature that distinguishes ou…

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…

math.OC20214 cited

Inexact Sequential Quadratic Optimization for Minimizing a Stochastic Objective Function Subject to Deterministic Nonlinear Equality Constraints

Frank E. Curtis, Daniel P. Robinson, Baoyu Zhou

An algorithm is proposed, analyzed, and tested experimentally for solving stochastic optimization problems in which the decision variables are constrained to satisfy equations defi…

math.OC2020

A Subspace Acceleration Method for Minimization Involving a Group Sparsity-Inducing Regularizer

Frank E. Curtis, Yutong Dai, Daniel P. Robinson

We consider the problem of minimizing an objective function that is the sum of a convex function and a group sparsity-inducing regularizer. Problems that integrate such regularizer…

math.OC20206 cited

Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization

Albert Berahas, Frank E. Curtis, Daniel P. Robinson +1

Sequential quadratic optimization algorithms are proposed for solving smooth nonlinear optimization problems with equality constraints. The main focus is an algorithm proposed for…

math.OC2019

Trust-Region Newton-CG with Strong Second-Order Complexity Guarantees for Nonconvex Optimization

Frank E. Curtis, Daniel P. Robinson, Clément Royer +1

Worst-case complexity guarantees for nonconvex optimization algorithms have been a topic of growing interest. Multiple frameworks that achieve the best known complexity bounds amon…