6 papers · 1 filter
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…
A Self-Correcting Variable-Metric Algorithm Framework for Nonsmooth Optimization
Frank E. Curtis, Daniel P. Robinson, Baoyu Zhou
An algorithm framework is proposed for minimizing nonsmooth functions. The framework is variable-metric in that, in each iteration, a step is computed using a symmetric positive de…
An Inexact Regularized Newton Framework with a Worst-Case Iteration Complexity of for Nonconvex Optimization
Frank E. Curtis, Daniel P. Robinson, Mohammadreza Samadi
An algorithm for solving smooth nonconvex optimization problems is proposed that, in the worst-case, takes iterations to drive the norm of the gradient of t…