DOI badge

CITATION.bib

@@ -4,5 +4,5 @@ @Misc{baraldi-orban-regularized-optimization-2022
   month = {February},
   howpublished = {\url{https://github.com/JuliaSmoothOptimizers/RegularizedOptimization.jl}},
   year = {2022},
-  DOI = {},
+  DOI = {10.5281/zenodo.6940313},
 }

README.md

@@ -3,6 +3,11 @@
 [![CI](https://github.com/JuliaSmoothOptimizers/RegularizedOptimization.jl/actions/workflows/ci.yml/badge.svg)](https://github.com/JuliaSmoothOptimizers/RegularizedOptimization.jl/actions/workflows/ci.yml)
 [![](https://img.shields.io/badge/docs-latest-3f51b5.svg)](https://JuliaSmoothOptimizers.github.io/RegularizedOptimization.jl/dev)
 [![codecov](https://codecov.io/gh/JuliaSmoothOptimizers/RegularizedOptimization.jl/branch/master/graph/badge.svg?token=lTbRmyBspS)](https://codecov.io/gh/JuliaSmoothOptimizers/RegularizedOptimization.jl)
+[![DOI](https://zenodo.org/badge/160387219.svg)](https://zenodo.org/badge/latestdoi/160387219)
+
+## How to cite
+
+If you use RegularizedOptimization.jl in your work, please cite using the format given in [CITATION.bib](CITATION.bib).
 
 ## Synopsis
 
@@ -35,6 +40,19 @@ Please refer to the documentation.
 
 ## References
 
-1. A. Y. Aravkin, R. Baraldi and D. Orban, *A Proximal Quasi-Newton Trust-Region Method for Nonsmooth Regularized Optimization*, Cahier du GERAD G-2021-12, GERAD, Montréal, Canada. https://arxiv.org/abs/2103.15993
+1. A. Y. Aravkin, R. Baraldi and D. Orban, *A Proximal Quasi-Newton Trust-Region Method for Nonsmooth Regularized Optimization*, SIAM Journal on Optimization, 32(2), pp.900–929, 2022. Technical report: https://arxiv.org/abs/2103.15993
 2. R. Baraldi, R. Kumar, and A. Aravkin (2019), [*Basis Pursuit De-noise with Non-smooth Constraints*](https://doi.org/10.1109/TSP.2019.2946029), IEEE Transactions on Signal Processing, vol. 67, no. 22, pp. 5811-5823.
 
+```bibtex
+@article{aravkin-baraldi-orban-2022,
+  author = {Aravkin, Aleksandr Y. and Baraldi, Robert and Orban, Dominique},
+  title = {A Proximal Quasi-{N}ewton Trust-Region Method for Nonsmooth Regularized Optimization},
+  journal = {SIAM Journal on Optimization},
+  volume = {32},
+  number = {2},
+  pages = {900--929},
+  year = {2022},
+  doi = {10.1137/21M1409536},
+  abstract = { We develop a trust-region method for minimizing the sum of a smooth term (f) and a nonsmooth term (h), both of which can be nonconvex. Each iteration of our method minimizes a possibly nonconvex model of (f + h) in a trust region. The model coincides with (f + h) in value and subdifferential at the center. We establish global convergence to a first-order stationary point when (f) satisfies a smoothness condition that holds, in particular, when it has a Lipschitz-continuous gradient, and (h) is proper and lower semicontinuous. The model of (h) is required to be proper, lower semi-continuous and prox-bounded. Under these weak assumptions, we establish a worst-case (O(1/\epsilon^2)) iteration complexity bound that matches the best known complexity bound of standard trust-region methods for smooth optimization. We detail a special instance, named TR-PG, in which we use a limited-memory quasi-Newton model of (f) and compute a step with the proximal gradient method, resulting in a practical proximal quasi-Newton method. We establish similar convergence properties and complexity bound for a quadratic regularization variant, named R2, and provide an interpretation as a proximal gradient method with adaptive step size for nonconvex problems. R2 may also be used to compute steps inside the trust-region method, resulting in an implementation named TR-R2. We describe our Julia implementations and report numerical results on inverse problems from sparse optimization and signal processing. Both TR-PG and TR-R2 exhibit promising performance and compare favorably with two linesearch proximal quasi-Newton methods based on convex models. }
+}
+```