Frank E. Curtis

According to our database, Frank E. Curtis authored at least 60 papers between 2004 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Fair machine learning through constrained stochastic optimization and an ε-constraint method.

Frank E. Curtis, Suyun Liu, Daniel P. Robinson

Optim. Lett., December, 2024

Worst-case complexity of an SQP method for nonlinear equality constrained stochastic optimization.

Frank E. Curtis, Michael O'Neill, Daniel P. Robinson

Math. Program., May, 2024

Sequential Quadratic Optimization for Stochastic Optimization with Deterministic Nonlinear Inequality and Equality Constraints.

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

SIAM J. Optim., 2024

Incremental quasi-Newton algorithms for solving a nonconvex, nonsmooth, finite-sum optimization problem.

Gulcin Dinc Yalcin, Frank E. Curtis

Optim. Methods Softw., 2024

Derivative-free bound-constrained optimization for solving structured problems with surrogate models.

Frank E. Curtis, Shima Dezfulian, Andreas Wächter

Optim. Methods Softw., 2024

Using Synthetic Data to Mitigate Unfairness and Preserve Privacy through Single-Shot Federated Learning.

Chia-Yuan Wu, Frank E. Curtis, Daniel P. Robinson

CoRR, 2024

Single-Loop Deterministic and Stochastic Interior-Point Algorithms for Nonlinearly Constrained Optimization.

Frank E. Curtis, Xin Jiang, Qi Wang

CoRR, 2024

2023

Worst-Case Complexity of TRACE with Inexact Subproblem Solutions for Nonconvex Smooth Optimization.

Frank E. Curtis, Qi Wang

SIAM J. Optim., September, 2023

An inexact column-and-constraint generation method to solve two-stage robust optimization problems.

Man Yiu Tsang, Karmel S. Shehadeh, Frank E. Curtis

Oper. Res. Lett., January, 2023

A Decomposition Algorithm with Fast Identification of Critical Contingencies for Large-Scale Security-Constrained AC-OPF.

Frank E. Curtis, Daniel K. Molzahn, Shenyinying Tu, Andreas Wächter, Ermin Wei, Elizabeth Wong

Oper. Res., 2023

Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition.

Ignacio Aravena, Daniel K. Molzahn, Shixuan Zhang, Cosmin G. Petra, Frank E. Curtis, Shenyinying Tu, Andreas Wächter, Ermin Wei, Elizabeth Wong, Amin Gholami, Kaizhao Sun, Xu Andy Sun, Stephen T. Elbert, Jesse Holzer, Arun Veeramany

Oper. Res., 2023

Almost-sure convergence of iterates and multipliers in stochastic sequential quadratic optimization.

Frank E. Curtis, Xin Jiang, Qi Wang

CoRR, 2023

A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems.

Frank E. Curtis, Vyacheslav Kungurtsev, Daniel P. Robinson, Qi Wang

CoRR, 2023

A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization.

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Gradient Sampling Methods with Inexact Subproblem Solutions and Gradient Aggregation.

Frank E. Curtis, Minhan Li

INFORMS J. Optim., October, 2022

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

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

SIAM J. Optim., 2022

A fully stochastic second-order trust region method.

Frank E. Curtis, Rui Shi

Optim. Methods Softw., 2022

Limited-memory BFGS with displacement aggregation.

Albert S. Berahas, Frank E. Curtis, Baoyu Zhou

Math. Program., 2022

2021

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

Frank E. Curtis, Daniel P. Robinson, Clément W. Royer, Stephen J. Wright

SIAM J. Optim., 2021

Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization.

Albert S. Berahas, Frank E. Curtis, Daniel P. Robinson, Baoyu Zhou

SIAM J. Optim., 2021

An accelerated communication-efficient primal-dual optimization framework for structured machine learning.

Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, Martin Takác

Optim. Methods Softw., 2021

Regional complexity analysis of algorithms for nonconvex smooth optimization.

Frank E. Curtis, Daniel P. Robinson

Math. Program., 2021

2020

Adaptive Stochastic Optimization: A Framework for Analyzing Stochastic Optimization Algorithms.

Frank E. Curtis, Katya Scheinberg

IEEE Signal Process. Mag., 2020

Inexact Sequential Quadratic Optimization with Penalty Parameter Updates within the QP Solver.

James V. Burke, Frank E. Curtis, Hao Wang, Jiashan Wang

SIAM J. Optim., 2020

ADMM for multiaffine constrained optimization.

Wenbo Gao, Donald Goldfarb, Frank E. Curtis

Optim. Methods Softw., 2020

Adaptive Stochastic Optimization.

Frank E. Curtis, Katya Scheinberg

CoRR, 2020

2019

A Stochastic Trust Region Algorithm Based on Careful Step Normalization.

Frank E. Curtis, Katya Scheinberg, Rui Shi

INFORMS J. Optim., July, 2019

Exploiting negative curvature in deterministic and stochastic optimization.

Frank E. Curtis, Daniel P. Robinson

Math. Program., 2019

2018

Optimization Methods for Large-Scale Machine Learning.

Léon Bottou, Frank E. Curtis, Jorge Nocedal

SIAM Rev., 2018

A Sequential Algorithm for Solving Nonlinear Optimization Problems with Chance Constraints.

Frank E. Curtis, Andreas Wächter, Victor M. Zavala

SIAM J. Optim., 2018

Complexity Analysis of a Trust Funnel Algorithm for Equality Constrained Optimization.

Frank E. Curtis, Daniel P. Robinson, Mohammadreza Samadi

SIAM J. Optim., 2018

FaRSA for ℓ1-regularized convex optimization: local convergence and numerical experience.

Tianyi Chen, Frank E. Curtis, Daniel P. Robinson

Optim. Methods Softw., 2018

Concise complexity analyses for trust region methods.

Frank E. Curtis, Zachary Lubberts, Daniel P. Robinson

Optim. Lett., 2018

2017

A Reduced-Space Algorithm for Minimizing ℓ₁-Regularized Convex Functions.

Tianyi Chen, Frank E. Curtis, Daniel P. Robinson

SIAM J. Optim., 2017

A BFGS-SQP method for nonsmooth, nonconvex, constrained optimization and its evaluation using relative minimization profiles.

Frank E. Curtis, Tim Mitchell, Michael L. Overton

Optim. Methods Softw., 2017

A trust region algorithm with a worst-case iteration complexity of O(ϵ ^-3/2) for nonconvex optimization.

Frank E. Curtis, Daniel P. Robinson, Mohammadreza Samadi

Math. Program., 2017

An interior-point trust-funnel algorithm for nonlinear optimization.

Frank E. Curtis, Nicholas I. M. Gould, Daniel P. Robinson, Philippe L. Toint

Math. Program., 2017

Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning.

Frank E. Curtis, Katya Scheinberg

CoRR, 2017

Solving nearly-separable quadratic optimization problems as nonsmooth equations.

Frank E. Curtis, Arvind U. Raghunathan

Comput. Optim. Appl., 2017

2016

Globally Convergent Primal-Dual Active-Set Methods with Inexact Subproblem Solves.

Frank E. Curtis, Zheng Han

SIAM J. Optim., 2016

Adaptive augmented Lagrangian methods: algorithms and practical numerical experience.

Frank E. Curtis, Nicholas I. M. Gould, Hao Jiang, Daniel P. Robinson

Optim. Methods Softw., 2016

A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization.

Frank E. Curtis

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Iterative Reweighted Linear Least Squares for Exact Penalty Subproblems on Product Sets.

James V. Burke, Frank E. Curtis, Hao Wang, Jiashan Wang

SIAM J. Optim., 2015

A quasi-Newton algorithm for nonconvex, nonsmooth optimization with global convergence guarantees.

Frank E. Curtis, Xiaocun Que

Math. Program. Comput., 2015

An adaptive augmented Lagrangian method for large-scale constrained optimization.

Frank E. Curtis, Hao Jiang, Daniel P. Robinson

Math. Program., 2015

Primal-Dual Active-Set Methods for Isotonic Regression and Trend Filtering.

Zheng Han, Frank E. Curtis

CoRR, 2015

A globally convergent primal-dual active-set framework for large-scale convex quadratic optimization.

Frank E. Curtis, Zheng Han, Daniel P. Robinson

Comput. Optim. Appl., 2015

2014

An Inexact Sequential Quadratic Optimization Algorithm for Nonlinear Optimization.

Frank E. Curtis, Travis C. Johnson, Daniel P. Robinson, Andreas Wächter

SIAM J. Optim., 2014

A Sequential Quadratic Optimization Algorithm with Rapid Infeasibility Detection.

James V. Burke, Frank E. Curtis, Hao Wang

SIAM J. Optim., 2014

2013

An adaptive gradient sampling algorithm for non-smooth optimization.

Frank E. Curtis, Xiaocun Que

Optim. Methods Softw., 2013

2012

A Sequential Quadratic Programming Algorithm for Nonconvex, Nonsmooth Constrained Optimization.

Frank E. Curtis, Michael L. Overton

SIAM J. Optim., 2012

A penalty-interior-point algorithm for nonlinear constrained optimization.

Frank E. Curtis

Math. Program. Comput., 2012

A note on the implementation of an interior-point algorithm for nonlinear optimization with inexact step computations.

Frank E. Curtis, Johannes Huber, Olaf Schenk, Andreas Wächter

Math. Program., 2012

2010

An Interior-Point Algorithm for Large-Scale Nonlinear Optimization with Inexact Step Computations.

Frank E. Curtis, Olaf Schenk, Andreas Wächter

SIAM J. Sci. Comput., 2010

Infeasibility Detection and SQP Methods for Nonlinear Optimization.

Richard H. Byrd, Frank E. Curtis, Jorge Nocedal

SIAM J. Optim., 2010

An inexact Newton method for nonconvex equality constrained optimization.

Richard H. Byrd, Frank E. Curtis, Jorge Nocedal

Math. Program., 2010

2009

A Matrix-Free Algorithm for Equality Constrained Optimization Problems with Rank-Deficient Jacobians.

Frank E. Curtis, Jorge Nocedal, Andreas Wächter

SIAM J. Optim., 2009

2008

An Inexact SQP Method for Equality Constrained Optimization.

Richard H. Byrd, Frank E. Curtis, Jorge Nocedal

SIAM J. Optim., 2008

2007

Steplength selection in interior-point methods for quadratic programming.

Frank E. Curtis, Jorge Nocedal

Appl. Math. Lett., 2007

2004

Central groupoids, central digraphs, and zero-one matrices A satisfying A²=J .

Frank E. Curtis, John Drew, Chi-Kwong Li, Daniel Pragel

J. Comb. Theory A, 2004