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)

Steplength selection in interior-point methods for quadratic programming. - Frank E. Curtis; Jorge Nocedal (Appl. Math. Lett., 2007)

An Inexact SQP Method for Equality Constrained Optimization. - Richard H. Byrd; Frank E. Curtis; Jorge Nocedal (SIAM J. Optim., 2008)

A Matrix-Free Algorithm for Equality Constrained Optimization Problems with Rank-Deficient Jacobians. - Frank E. Curtis; Jorge Nocedal; Andreas Wächter (SIAM J. Optim., 2009)

An inexact Newton method for nonconvex equality constrained optimization. - Richard H. Byrd; Frank E. Curtis; Jorge Nocedal (Math. Program., 2010)

Infeasibility Detection and SQP Methods for Nonlinear Optimization. - Richard H. Byrd; Frank E. Curtis; Jorge Nocedal (SIAM J. Optim., 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)

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)

A penalty-interior-point algorithm for nonlinear constrained optimization. - Frank E. Curtis (Math. Program. Comput., 2012)

A Sequential Quadratic Programming Algorithm for Nonconvex, Nonsmooth Constrained Optimization. - Frank E. Curtis; Michael L. Overton (SIAM J. Optim., 2012)

An adaptive gradient sampling algorithm for non-smooth optimization. - Frank E. Curtis; Xiaocun Que (Optim. Methods Softw., 2013)

A Sequential Quadratic Optimization Algorithm with Rapid Infeasibility Detection. - James V. Burke; Frank E. Curtis; Hao Wang 045 (SIAM J. Optim., 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 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)

Primal-Dual Active-Set Methods for Isotonic Regression and Trend Filtering. - Zheng Han; Frank E. Curtis (CoRR, 2015)

An adaptive augmented Lagrangian method for large-scale constrained optimization. - Frank E. Curtis; Hao Jiang; Daniel P. Robinson (Math. Program., 2015)

A quasi-Newton algorithm for nonconvex, nonsmooth optimization with global convergence guarantees. - Frank E. Curtis; Xiaocun Que (Math. Program. Comput., 2015)

Iterative Reweighted Linear Least Squares for Exact Penalty Subproblems on Product Sets. - James V. Burke; Frank E. Curtis; Hao Wang 045; Jiashan Wang (SIAM J. Optim., 2015)

A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization. - Frank E. Curtis (ICML, 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)

Globally Convergent Primal-Dual Active-Set Methods with Inexact Subproblem Solves. - Frank E. Curtis; Zheng Han (SIAM J. Optim., 2016)

Solving nearly-separable quadratic optimization problems as nonsmooth equations. - Frank E. Curtis; Arvind U. Raghunathan (Comput. Optim. Appl., 2017)

Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning. - Frank E. Curtis; Katya Scheinberg (CoRR, 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)

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)

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 Reduced-Space Algorithm for Minimizing ℓ1-Regularized Convex Functions. - Tianyi Chen; Frank E. Curtis; Daniel P. Robinson (SIAM J. Optim., 2017)

Concise complexity analyses for trust region methods. - Frank E. Curtis; Zachary Lubberts; Daniel P. Robinson (Optim. Lett., 2018)

FaRSA for ℓ1-regularized convex optimization: local convergence and numerical experience. - Tianyi Chen; Frank E. Curtis; Daniel P. Robinson (Optim. Methods Softw., 2018)

Complexity Analysis of a Trust Funnel Algorithm for Equality Constrained Optimization. - Frank E. Curtis; Daniel P. Robinson; Mohammadreza Samadi (SIAM J. Optim., 2018)

A Sequential Algorithm for Solving Nonlinear Optimization Problems with Chance Constraints. - Frank E. Curtis; Andreas Wächter; Victor M. Zavala (SIAM J. Optim., 2018)

Optimization Methods for Large-Scale Machine Learning. - Léon Bottou; Frank E. Curtis; Jorge Nocedal (SIAM Rev., 2018)

Exploiting negative curvature in deterministic and stochastic optimization. - Frank E. Curtis; Daniel P. Robinson (Math. Program., 2019)

A Stochastic Trust Region Algorithm Based on Careful Step Normalization. - Frank E. Curtis; Katya Scheinberg; Rui Shi (INFORMS J. Optim., 2019)

Adaptive Stochastic Optimization. - Frank E. Curtis; Katya Scheinberg (CoRR, 2020)

ADMM for multiaffine constrained optimization. - Wenbo Gao; Donald Goldfarb; Frank E. Curtis (Optim. Methods Softw., 2020)

Inexact Sequential Quadratic Optimization with Penalty Parameter Updates within the QP Solver. - James V. Burke; Frank E. Curtis; Hao Wang 045; Jiashan Wang (SIAM J. Optim., 2020)

Adaptive Stochastic Optimization: A Framework for Analyzing Stochastic Optimization Algorithms. - Frank E. Curtis; Katya Scheinberg (IEEE Signal Process. Mag., 2020)

Regional complexity analysis of algorithms for nonconvex smooth optimization. - Frank E. Curtis; Daniel P. Robinson (Math. Program., 2021)

An accelerated communication-efficient primal-dual optimization framework for structured machine learning. - Chenxin Ma; Martin Jaggi; Frank E. Curtis; Nathan Srebro; Martin Takác 001 (Optim. Methods Softw., 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)

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 001 (SIAM J. Optim., 2021)

Limited-memory BFGS with displacement aggregation. - Albert S. Berahas; Frank E. Curtis; Baoyu Zhou (Math. Program., 2022)

A fully stochastic second-order trust region method. - Frank E. Curtis; Rui Shi (Optim. Methods Softw., 2022)

A Subspace Acceleration Method for Minimization Involving a Group Sparsity-Inducing Regularizer. - Frank E. Curtis; Yutong Dai 002; Daniel P. Robinson (SIAM J. Optim., 2022)

Gradient Sampling Methods with Inexact Subproblem Solutions and Gradient Aggregation. - Frank E. Curtis; Minhan Li (INFORMS J. Optim., 2022)

A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization. - Yutong Dai 002; Guanyi Wang; Frank E. Curtis; Daniel P. Robinson (AISTATS, 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; et al. (Oper. Res., 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)

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., 2023)

Worst-Case Complexity of TRACE with Inexact Subproblem Solutions for Nonconvex Smooth Optimization. - Frank E. Curtis; Qi Wang (SIAM J. Optim., 2023)

Single-Loop Deterministic and Stochastic Interior-Point Algorithms for Nonlinearly Constrained Optimization. - Frank E. Curtis; Xin Jiang; Qi Wang (CoRR, 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)

A Stochastic Inexact Sequential Quadratic Optimization Algorithm for Nonlinear Equality-Constrained Optimization. - Frank E. Curtis; Daniel P. Robinson; Baoyu Zhou (INFORMS J. Optim., 2024)

A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear-Equality-Constrained Optimization with Rank-Deficient Jacobians. - Albert S. Berahas; Frank E. Curtis; Michael O'Neill 002; Daniel P. Robinson (Math. Oper. Res., 2024)

Derivative-free bound-constrained optimization for solving structured problems with surrogate models. - Frank E. Curtis; Shima Dezfulian; Andreas Wächter (Optim. Methods Softw., 2024)

Incremental quasi-Newton algorithms for solving a nonconvex, nonsmooth, finite-sum optimization problem. - Gulcin Dinc Yalcin; Frank E. Curtis (Optim. Methods Softw., 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)

Worst-case complexity of an SQP method for nonlinear equality constrained stochastic optimization. - Frank E. Curtis; Michael J. O'Neill 002; Daniel P. Robinson (Math. Program., 2024)

Fair machine learning through constrained stochastic optimization and an ε-constraint method. - Frank E. Curtis; Suyun Liu; Daniel P. Robinson (Optim. Lett., 2024)

Stochastic Optimization Approaches for an Operating Room and Anesthesiologist Scheduling Problem. - Man Yiu Tsang; Karmel S. Shehadeh; Frank E. Curtis; Beth R. Hochman; Tricia E. Brentjens (Oper. Res., 2025)

A Stochastic-Gradient-Based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems. - Frank E. Curtis; Vyacheslav Kungurtsev; Daniel P. Robinson; Qi Wang (SIAM J. Optim., 2025)

An Interior-Point Algorithm for Continuous Nonlinearly Constrained Optimization with Noisy Function and Derivative Evaluations. - Frank E. Curtis; Shima Dezfulian; Andreas Wächter (CoRR, 2025)

Almost-Sure Convergence of Iterates and Multipliers in Stochastic Sequential Quadratic Optimization. - Frank E. Curtis; Xin Jiang; Qi Wang (J. Optim. Theory Appl., 2025)

Fair Supervised Learning Through Constraints on Smooth Nonconvex Unfairness-Measure Surrogates. - Zahra Khatti; Daniel P. Robinson; Frank E. Curtis (CoRR, 2025)