This is a version 1.0 of NonOpt. If you encounter any significant bug(s), then please contact Frank E. Curtis.
NonOpt (Nonlinear/nonconvex/nonsmooth Optimizer) is a software package for solving minimization problems. It is designed to locate a stationary point (ideally, a minimizer) of
min f(x)
x ∈ Rⁿ
where f : Rⁿ -> R is locally Lipschitz over Rⁿ and continuously differentiable over a full-measure subset of Rⁿ. The function f can be nonlinear, nonconvex, and/or nonsmooth.
NonOpt is written in C++ and is released under the MIT License. The main author is Frank E. Curtis. For a list of all contributors, please see the AUTHORS file.
Compiling NonOpt requires BLAS and LAPACK. The code for these packages is not provided in this repository and must be obtained separately by a user.
Please visit the NonOpt homepage.
NonOpt is provided free of charge so that it might be useful to others. Please send e-mail to Frank E. Curtis with success stories or other feedback. If you use NonOpt in your research, then please cite the following papers:
- F. E. Curtis and M. Li. "Gradient Sampling Methods with Inexact Subproblem Solutions and Gradient Aggregation." arXiv:2005.07822 (to appear in INFORMS Journal on Optimization), 2020.
- F. E. Curtis, D. P. Robinson, and B. Zhou. "A Self-Correcting Variable-Metric Algorithm Framework for Nonsmooth Optimization." IMA Journal of Numerical Analysis, 10.1093/imanum/drz008, 2019.
- F. E. Curtis and X. Que. "A Quasi-Newton Algorithm for Nonconvex, Nonsmooth Optimization with Global Convergence Guarantees." Mathematical Programming Computation, 7(4):399–428, 2015.
- F. E. Curtis and X. Que. "An Adaptive Gradient Sampling Algorithm for Nonsmooth Optimization." Optimization Methods and Software, 28(6):1302–1324, 2013.