30 citations · 108 across the 43 of their papers we have counts for
48 papers
A Local-Linearly Convergent Algorithm for Nonconvex Equality-Constrained Optimization
Frank E. Curtis, Lingjun Guo, Daniel P. Robinson
For solving nonconvex equality-constrained optimization problems, a recent Gradient-Eigenstep Algorithm by Goyens et al.~is an iteration-efficient approach, based on minimizing Fle…
A Proximal-Gradient Method for Solving Regularized Optimization Problems with General Constraints
Frank E. Curtis, Xiaoyi Qu, Daniel P. Robinson
We propose, analyze, and test a proximal-gradient method for solving regularized optimization problems with general constraints. The method employs a decomposition strategy to comp…
Progressively Sampled Equality-Constrained Optimization
Frank E. Curtis, Lingjun Guo, Daniel P. Robinson
An algorithm is proposed, analyzed, and tested for solving continuous nonlinear-equality-constrained optimization problems where the objective and constraint functions are defined…
Active-Set Identification in Noisy and Stochastic Optimization
Frank E. Curtis, Daniel P. Robinson, Lara Zebiane
Identifying active constraints from a point near a local minimizer is important both theoretically and practically in constrained continuous optimization as it can help identify op…
Fair Supervised Learning Through Constraints on Smooth Nonconvex Unfairness-Measure Surrogates
Zahra Khatti, Daniel P. Robinson, Frank E. Curtis
A new strategy for fair supervised machine learning is proposed. The main advantages of the proposed strategy as compared to others in the literature are as follows. (a) We introdu…
NonOpt: Nonconvex, Nonsmooth Optimizer
Frank E. Curtis, Lara Zebiane
NonOpt, a C++ software package for minimizing locally Lipschitz objective functions, is presented. The software is intended primarily for minimizing objective functions that are no…