update bibliography entries in docs (#84)

Author: aldma

docs/references.bib

@@ -164,19 +164,24 @@ @article{Themelis2018
   url={https://epubs.siam.org/doi/10.1137/16M1080240}
 }
 
-@article{Themelis2020,
-  title={Douglas-Rachford splitting and ADMM for nonconvex optimization: Accelerated and Newton-type algorithms},
+@article{Themelis2022,
+  title={Douglas-Rachford splitting and ADMM for nonconvex optimization: Accelerated and Newton-type linesearch algorithms},
   author={Themelis, Andreas and Stella, Lorenzo and Patrinos, Panagiotis},
-  journal={arXiv preprint arXiv:2005.10230},
-  year={2020},
-  url={https://arxiv.org/abs/2005.10230}
+  journal={Computational Optimization and Applications},
+  volume={82},
+  number={2},
+  pages={395--440},
+  year={2022},
+  url={https://doi.org/10.1007/s10589-022-00366-y}
 }
 
-@misc{DeMarchi2021,
-  title={Proximal Gradient Algorithms under Local Lipschitz Gradient Continuity: A Convergence and Robustness Analysis of PANOC},
-  author={Alberto De Marchi and Andreas Themelis},
-  year={2021},
-  eprint={2112.13000},
-  archivePrefix={arXiv},
-  primaryClass={math.OC}
+@article{DeMarchi2022,
+  title={Proximal Gradient Algorithms under Local Lipschitz Gradient Continuity},
+  author={De Marchi, Alberto and Themelis, Andreas},
+  journal={Journal of Optimization Theory and Applications},
+  volume={194},
+  number={3},
+  pages={771--794},
+  year={2022},
+  url={https://doi.org/10.48550/arXiv.2112.13000}
 }

docs/src/guide/implemented_algorithms.md

@@ -29,8 +29,8 @@ Douglas-Rachford | | ``\operatorname{prox}_{\gamma f}``, ``\operatorname{prox}_{
 Fast proximal gradient | ``f`` convex, smooth, ``g`` convex | ``\nabla f``, ``\operatorname{prox}_{\gamma g}`` | [`FastForwardBackward`](@ref) | [Tseng2008](@cite), [Beck2009](@cite)
 PANOC | ``f`` smooth | ``\nabla f``, ``\operatorname{prox}_{\gamma g}`` | [`PANOC`](@ref) | [Stella2017](@cite)
 ZeroFPR | ``f`` smooth | ``\nabla f``, ``\operatorname{prox}_{\gamma g}`` | [`ZeroFPR`](@ref) | [Themelis2018](@cite)
-Douglas-Rachford line-search | ``f`` smooth | ``\operatorname{prox}_{\gamma f}``, ``\operatorname{prox}_{\gamma g}`` | [`DRLS`](@ref) | [Themelis2020](@cite)
-PANOC+ | ``f`` locally smooth | ``\nabla f``, ``\operatorname{prox}_{\gamma g}`` | [`PANOCplus`](@ref) | [DeMarchi2021](@cite)
+Douglas-Rachford line-search | ``f`` smooth | ``\operatorname{prox}_{\gamma f}``, ``\operatorname{prox}_{\gamma g}`` | [`DRLS`](@ref) | [Themelis2022](@cite)
+PANOC+ | ``f`` locally smooth | ``\nabla f``, ``\operatorname{prox}_{\gamma g}`` | [`PANOCplus`](@ref) | [DeMarchi2022](@cite)
 
 ```@docs
 ProximalAlgorithms.ForwardBackward

src/algorithms/drls.jl

@@ -1,8 +1,6 @@
-# Themelis, Stella, Patrinos, "Douglas-Rachford splitting and ADMM
-# for nonconvex optimization: Accelerated and Newton-type linesearch algorithms",
-# arXiv:2005.10230, 2020.
-#
-# https://arxiv.org/abs/2005.10230
+# Themelis, Stella, Patrinos, "Douglas-Rachford splitting and ADMM for
+# nonconvex optimization: Accelerated and Newton-type linesearch algorithms",
+# Computational Optimization and Applications, vol. 82, no. 2, pp. 395-440 (2022).
 
 using Base.Iterators
 using ProximalAlgorithms.IterationTools
@@ -47,7 +45,7 @@ See also: [`DRLS`](@ref).
 - `directions=LBFGS(5)`: strategy to use to compute line-search directions.
 
 # References
-1. Themelis, Stella, Patrinos, "Douglas-Rachford splitting and ADMM for nonconvex optimization: Accelerated and Newton-type linesearch algorithms", arXiv:2005.10230, 2020.
+1. Themelis, Stella, Patrinos, "Douglas-Rachford splitting and ADMM for nonconvex optimization: Accelerated and Newton-type linesearch algorithms", Computational Optimization and Applications, vol. 82, no. 2, pp. 395-440 (2022).
 """
 Base.@kwdef struct DRLSIteration{R,Tx,Tf,Tg,Tmf,TLf,D}
     f::Tf = Zero()
@@ -216,7 +214,7 @@ See also: [`DRLSIteration`](@ref), [`IterativeAlgorithm`](@ref).
 - `kwargs...`: additional keyword arguments to pass on to the `DRLSIteration` constructor upon call
 
 # References
-1. Themelis, Stella, Patrinos, "Douglas-Rachford splitting and ADMM for nonconvex optimization: Accelerated and Newton-type linesearch algorithms", arXiv:2005.10230, 2020.
+1. Themelis, Stella, Patrinos, "Douglas-Rachford splitting and ADMM for nonconvex optimization: Accelerated and Newton-type linesearch algorithms", Computational Optimization and Applications, vol. 82, no. 2, pp. 395-440 (2022).
 """
 DRLS(;
     maxit=1_000,

src/algorithms/panocplus.jl

@@ -1,6 +1,6 @@
-# De Marchi, Themelis, "Proximal gradient algorithms under local Lipschitz
-# gradient continuity: a convergence and robustness analysis of PANOC",
-# arXiv:2112.13000 (2021).
+# De Marchi, Themelis, "Proximal Gradient Algorithms under Local Lipschitz
+# Gradient Continuity", Journal of Optimization Theory and Applications, 
+# vol. 194, no. 3, pp. 771-794 (2022).
 
 using Base.Iterators
 using ProximalAlgorithms.IterationTools
@@ -34,7 +34,7 @@ See also: [`PANOCplus`](@ref).
 - `directions=LBFGS(5)`: strategy to use to compute line-search directions.
 
 # References
-1. De Marchi, Themelis, "Proximal gradient algorithms under local Lipschitz gradient continuity: a convergence and robustness analysis of PANOC", arXiv:2112.13000 (2021).
+1. De Marchi, Themelis, "Proximal Gradient Algorithms under Local Lipschitz Gradient Continuity", Journal of Optimization Theory and Applications, vol. 194, no. 3, pp. 771-794 (2022).
 """
 Base.@kwdef struct PANOCplusIteration{R,Tx,Tf,TA,Tg,TLf,Tgamma,D}
     f::Tf = Zero()
@@ -225,7 +225,7 @@ See also: [`PANOCplusIteration`](@ref), [`IterativeAlgorithm`](@ref).
 - `kwargs...`: additional keyword arguments to pass on to the `PANOCplusIteration` constructor upon call
 
 # References
-1. De Marchi, Themelis, "Proximal gradient algorithms under local Lipschitz gradient continuity: a convergence and robustness analysis of PANOC", arXiv:2112.13000 (2021).
+1. De Marchi, Themelis, "Proximal Gradient Algorithms under Local Lipschitz Gradient Continuity", Journal of Optimization Theory and Applications, vol. 194, no. 3, pp. 771-794 (2022).
 """
 PANOCplus(;
     maxit=1_000,