6 citations · 26 across the 14 of their papers we have counts for
5 papers · 1 filter
Gradient Sampling Methods for Nonsmooth Optimization
James V. Burke, Frank E. Curtis, Adrian S. Lewis +2
This paper reviews the gradient sampling methodology for solving nonsmooth, nonconvex optimization problems. An intuitively straightforward gradient sampling algorithm is stated an…
Inexact Sequential Quadratic Optimization with Penalty Parameter Updates Within the QP Solve: Extended Version
James V. Burke, Frank E. Curtis, Hao Wang +1
This paper focuses on the design of sequential quadratic optimization (commonly known as SQP) methods for solving large-scale nonlinear optimization problems. The most computationa…
Concise Complexity Analyses for Trust-Region Methods
Frank E. Curtis, Zachary Lubberts, Daniel P. Robinson
Concise complexity analyses are presented for simple trust region algorithms for solving unconstrained optimization problems. In contrast to a traditional trust region algorithm, t…
ADMM for Multiaffine Constrained Optimization
Wenbo Gao, Donald Goldfarb, Frank E. Curtis
We expand the scope of the alternating direction method of multipliers (ADMM). Specifically, we show that ADMM, when employed to solve problems with multiaffine constraints that sa…
Regional Complexity Analysis of Algorithms for Nonconvex Smooth Optimization
Frank E. Curtis, Daniel P. Robinson
A strategy is proposed for characterizing the worst-case performance of algorithms for solving nonconvex smooth optimization problems. Contemporary analyses characterize worst-case…