7 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…
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…
Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning
Chia-Yuan Wu, Frank E. Curtis, Daniel P. Robinson
Collaborative machine learning (CML) enables multiple clients to train a global model jointly in a data-distributed setting. To address data privacy and communication efficiency, o…
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…
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…
Active-Set Identification in Noisy and Stochastic Optimization
Frank E. Curtis, Daniel P. Robinson, Lara Zebiane
Identifying active constraints from a point near an optimal solution is important both theoretically and practically in constrained continuous optimization, as it can help identify…