JuliaSmoothOptimizers · MaxenceGollier · Oct 1, 2025 · Sep 12, 2025 · Sep 12, 2025 · Sep 12, 2025
diff --git a/docs/Project.toml b/docs/Project.toml
@@ -1,5 +1,7 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+DocumenterCitations = "daee34ce-89f3-4625-b898-19384cb65244"
 
 [compat]
-Documenter = "~0.25"
+Documenter = "1"
-Documenter = "1"
+Documenter = "1.0"
-Documenter = "1"
+Documenter = "1.0"
+DocumenterCitations = "1.2"
diff --git a/docs/make.jl b/docs/make.jl
@@ -1,16 +1,30 @@
-using Documenter, RegularizedOptimization
+using Documenter, DocumenterCitations 
+
+using RegularizedOptimization
+
+bib = CitationBibliography(joinpath(@__DIR__, "references.bib"))
 
 makedocs(
   modules = [RegularizedOptimization],
   doctest = true,
   # linkcheck = true,
-  strict = true,
+  warnonly = false,
-  warnonly = false,
+  strict = [:missing_docs],
-  warnonly = false,
+  strict = [:missing_docs],
   format = Documenter.HTML(
     assets = ["assets/style.css"],
     prettyurls = get(ENV, "CI", nothing) == "true",
   ),
   sitename = "RegularizedOptimization.jl",
-  pages = Any["Home" => "index.md", "Tutorial" => "tutorial.md", "Reference" => "reference.md"],
+  pages = [
+    "Home" => "index.md", 
+    "Algorithms" => "algorithms.md", 
+    "Examples" => [
+      joinpath("examples", "bpdn.md"),
+      joinpath("examples", "fh.md")
+    ], 
+    "Reference" => "reference.md",
+    "Bibliography" => "bibliography.md"
+    ],
+    plugins = [bib],
 )
 
 deploydocs(

diff --git a/docs/references.bib b/docs/references.bib
@@ -0,0 +1,56 @@
+@Article{aravkin-baraldi-orban-2022,
+  author        = {Aravkin, Aleksandr Y. and Baraldi, Robert and Orban, Dominique},
+  title         = {A Proximal Quasi-Newton 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}
+}
+
+@Article{aravkin-baraldi-orban-2024,
+  author        = {Aravkin, Aleksandr Y. and Baraldi, Robert and Orban, Dominique},
+  title         = {A Levenberg–Marquardt Method for Nonsmooth Regularized Least Squares},
+  journal       = {SIAM Journal on Scientific Computing},
+  volume        = {46},
+  number        = {4},
+  pages         = {A2557-A2581},
+  year          = {2024},
+  doi           = {10.1137/22M1538971},
+}
+
+@Article{leconte-orban-2025,
+  author        = {Leconte, Geoffroy and Orban, Dominique},
+  title         = {The indefinite proximal gradient method},
+  journal       = {Computational Optimization and Applications},
+  volume        = {91},
+  number        = {2},
+  pages         = {861-903},
+  year          = {2025},
+  doi           = {10.1007/s10589-024-00604-5}
+}
+
+@TechReport{diouane-gollier-orban-2024,
+  Author        = {Diouane, Youssef and Gollier, Maxence and Orban, Dominique},
+  Title         = {A nonsmooth exact penalty method for equality-constrained optimization: complexity and implementation},
+  Institution   = {Groupe d’études et de recherche en analyse des décisions},
+  Year          = {2024},
+  Type          = {Les Cahiers du GERAD},
+  Number        = {G-2024-65},
+  Address       = {Montreal, Canada},
+  doi           = {10.48550/arxiv.2103.15993},
+  url           = {https://www.gerad.ca/fr/papers/G-2024-65},
+}
+
+@TechReport{diouane-habiboullah-orban-2024,
+  Author        = {Diouane, Youssef and Laghdaf Habiboullah, Mohamed and Orban, Dominique},
+  Title         = {A proximal modified quasi-Newton method for nonsmooth regularized optimization},
+  Institution   = {Groupe d’études et de recherche en analyse des décisions},
+  Year          = {2024},
+  Type          = {Les Cahiers du GERAD},
+  Number        = {G-2024-64},
+  Address       = {Montreal, Canada},
+  doi           = {10.48550/arxiv.2409.19428},
+  url           = {https://www.gerad.ca/fr/papers/G-2024-64},
+}
diff --git a/docs/src/algorithms.md b/docs/src/algorithms.md
@@ -0,0 +1,67 @@
+# [Algorithms](@id algorithms)
+
+## General case
+The algorithms in this package are based upon the approach of [aravkin-baraldi-orban-2022](@cite).
+Suppose we are given the general regularized problem
+```math
+\underset{x \in \mathbb{R}^n}{\text{minimize}} \quad f(x) + h(x),
+```
+where $f : \mathbb{R}^n \mapsto \mathbb{R}$ is continuously differentiable and $h : \mathbb{R}^n \mapsto \mathbb{R} \cup \{\infty\}$ is lower semi-continuous.
+Instead of solving the above directly, which is often impossible, we will solve a simplified version of it repeatedly until we reach a minimizer of the problem above.
+To do so, suppose we are given an iterate $x_0 \in \mathbb{R}^n$, we wish to compute a step, $s_0 \in \mathbb{R}^n$ and improve our iterate with $x_1 := x_0 + s_0$.
+Now, we are going to approximate the functions $f$ and $h$ around $x_0$ with simpler functions (models), which we denote respectively $\varphi(\cdot; x_0)$ and $\psi(\cdot; x_0)$ so that
+```math
+\varphi(s; x_0) \approx f(x_0 + s) \quad \text{and} \quad \psi(s; x_0) \approx h(x_0 + s). 
+```
+We then wish to compute the step as
+```math
+s_0 \in \underset{s \in \mathbb{R}^n}{\argmin} \  \varphi(s; x_0) + \psi(s; x_0).
+```
+In order to ensure convergence and to handle the potential nonconvexity of the objective function, we either add a trust-region,
+```math
+s_0 \in \underset{s \in \mathbb{R}^n}{\argmin} \  \varphi(s; x_0) + \psi(s; x_0) \quad \text{subject to} \ \|s\| \leq \Delta,
+```
+or a quadratic regularization
+```math
+s_0 \in \underset{s \in \mathbb{R}^n}{\argmin} \  \varphi(s; x_0) + \psi(s; x_0) + \sigma \|s\|^2_2.
+```
+Algorithms that work with a trust-region are [`TR`](@ref TR) and [`TRDH`](@ref TRDH) and the ones working with a quadratic regularization are [`R2`](@ref R2), [`R2N`](@ref R2N) and [`R2DH`](@ref R2DH)
+
+The models for the smooth part `f` in this package are always quadratic models of the form
+```math
+\varphi(s; x_0) = f(x_0) + \nabla f(x_0)^T s + \frac{1}{2} s^T H(x_0) s,
+```
+where $H(x_0)$ is a symmetric matrix that can be either $0$, the Hessian of $f$ (if it exists) or a quasi-Newton approximation.
+Some algorithms require a specific structure for $H$, for an overview, refer to the table below.
+
+The following table gives an overview of the available algorithms in the general case.
+
+Algorithm | Quadratic Regularization | Trust Region | Quadratic term for $\varphi$ : H | Reference
+----------|--------------------------|--------------|---------------|----------
+[`R2`](@ref R2) | Yes | No | $H = 0$  | [aravkin-baraldi-orban-2022; Algorithm 6.1](@cite)
+[`R2N`](@ref R2N) | Yes | No | Any Symmetric| [diouane-habiboullah-orban-2024; Algorithm 1](@cite)
+[`R2DH`](@ref R2DH) | Yes | No | Any Diagonal | [diouane-habiboullah-orban-2024; Algorithm 1](@cite)
+[`TR`](@ref TR) | No | Yes | Any Symmetric | [aravkin-baraldi-orban-2022; Algorithm 3.1](@cite)
+[`TRDH`](@ref TRDH) | No | Yes | Any Diagonal | [leconte-orban-2025; Algorithm 5.1](@cite)
+
+## Nonlinear least-squares
+This package provides two algorithms, [`LM`](@ref LM) and [`LMTR`](@ref LMTR), specialized for regularized, nonlinear least-squares.
+That is, problems of the form
+```math
+\underset{x \in \mathbb{R}^n}{\text{minimize}} \quad \frac{1}{2}\|F(x)\|_2^2 + h(x),
+```
+where $F : \mathbb{R}^n \mapsto \mathbb{R}^m$ is continuously differentiable and $h : \mathbb{R}^n \mapsto \mathbb{R} \cup \{\infty\}$ is lower semi-continuous.
+In that case, the model $\varphi$ is defined as 
+```math
+\varphi(s; x) = \frac{1}{2}\|F(x) + J(x)s\|_2^2,
+```
+where $J(x)$ is the Jacobian of $F$ at $x$.
+Similar to the algorithms in the previous section, we either add a quadratic regularization to the model ([`LM`](@ref LM)) or a trust-region ([`LMTR`](@ref LMTR)).
+These algorithms are described in [aravkin-baraldi-orban-2024](@cite).
+
+## Constrained Optimization
+For constrained, regularized optimization,
+```math
+\underset{x \in \mathbb{R}^n}{\text{minimize}} \quad f(x) + h(x) \quad \text{subject to} \ l \leq x \leq u \ \text{and} \ c(x) = 0,
+```
+an augmented Lagrangian method is provided, [`AL`](@ref AL).
diff --git a/docs/src/bibliography.md b/docs/src/bibliography.md
@@ -0,0 +1,4 @@
+# Bibliography
+
+```@bibliography
+```
diff --git a/docs/src/examples/bpdn.md b/docs/src/examples/bpdn.md
diff --git a/docs/src/examples/fh.md b/docs/src/examples/fh.md
diff --git a/docs/src/index.md b/docs/src/index.md
@@ -1 +1,109 @@
 # RegularizedOptimization.jl
+
+This package implements a family of algorithms to solve nonsmooth optimization problems of the form
+
+```math
+\underset{x \in \mathbb{R}^n}{\text{minimize}} \quad f(x) + h(x),
+```
+
+where $f : \mathbb{R}^n \to \mathbb{R}$ is continuously differentiable and $h : \mathbb{R}^n \to \mathbb{R} \cup \{\infty\}$ is lower semi-continuous and proper.
+Both $f$ and $h$ may be **nonconvex**.
+
+All solvers implemented in this package are **JuliaSmoothOptimizers-compliant**.  
+They take a [`RegularizedNLPModel`](https://jso.dev/RegularizedProblems.jl/dev/reference#RegularizedProblems.RegularizedNLPModel) as input and return a [`GenericExecutionStats`](https://jso.dev/SolverCore.jl/stable/reference/#SolverCore.GenericExecutionStats).  
+
+A [`RegularizedNLPModel`](https://jso.dev/RegularizedProblems.jl/stable/reference#RegularizedProblems.RegularizedNLPModel) contains:  
+
+- a smooth component `f` represented as an [`AbstractNLPModel`](https://github.com/JuliaSmoothOptimizers/NLPModels.jl),  
+- a nonsmooth regularizer `h`.  
+
+We refer to [jso.dev](https://jso.dev) for tutorials on the `NLPModel` API. This framework allows the usage of models from  
+
+- AMPL ([AmplNLReader.jl](https://github.com/JuliaSmoothOptimizers/AmplNLReader.jl)),  
+- CUTEst ([CUTEst.jl](https://github.com/JuliaSmoothOptimizers/CUTEst.jl)),  
+- JuMP ([NLPModelsJuMP.jl](https://github.com/JuliaSmoothOptimizers/NLPModelsJuMP.jl)),  
+- PDE-constrained problems ([PDENLPModels.jl](https://github.com/JuliaSmoothOptimizers/PDENLPModels.jl)),  
+- models defined with automatic differentiation ([ADNLPModels.jl](https://github.com/JuliaSmoothOptimizers/ADNLPModels.jl)).
+
+We refer to [ManualNLPModels.jl](https://github.com/JuliaSmoothOptimizers/ManualNLPModels.jl) for users interested in defining their own model.
+
+---
+
+## Regularizers
+
+Regularizers used in this package are based on the [ShiftedProximalOperators.jl](https://github.com/JuliaSmoothOptimizers/ShiftedProximalOperators.jl) API, which is related to [ProximalOperators.jl](https://github.com/JuliaFirstOrder/ProximalOperators.jl). 
+
+The solvers in this package work by approximating the regularizer with a *shifted model*.
+That is, at each iterate $x_k$, we approximate $h(x_k + s)$ with a (simpler) function $\psi(s; x_k)$.
+For example, if $h(x) = \|x\|$, then its *shifted model* is simply the function $h$ itself : $\psi(s; x_k) = \|x_k + s\|$.
+On the other hand, if $h$ is the composition of a norm with a function, $h(x) = \|c(x)\|$, then its *shifted model* can be the approximation
+```math
+\psi(s; x_k) = \|c(x_k) + J(x_k)s\| \approx \|c(x_k + s) \| = h(x_k + s),
+```
+where $J(x_k)$ is the Jacobian of $c$ at the point $x_k$.
+
+Basically, we expect a regularizer `h::Foo` to
+
+- Be callable with vectors, i.e. to implement `(h::Foo)(x::AbstractVector)`.
+- Be *shifteable*, that is, to implement a function `shifted(h::Foo, x::AbstractVector)` that returns the shifted model `ψ::ShiftedFoo`.
+
+Next, we expect the shifted model `ψ::ShiftedFoo` to 
+
+- Be callable with vectors, i.e. to implement `(ψ::ShiftedFoo)(x::AbstractVector)`.
+- Be *shifteable*, that is, to implement a function `shifted(ψ::ShiftedFoo, x::AbstractVector)` that returns a shifted model `ψ'::ShiftedFoo`. Moreover, we should be able to change the shift in place, that is, the function `shift!(ψ::ShiftedFoo, x::AbstractVector)` should be implemented as well.
+- Be *proximable*, that is, to implement the inplace proximal mapping `prox!(y::AbstractVector, ψ::ShiftedFoo, q::AbstractVector, σ::Real)`.
+
+The proximal mapping is defined as 
+```math
+\text{prox}(\psi, q, \sigma) := \argmin_y \ \psi(y) + \frac{\sigma}{2} \|y - q\|_2^2.
+```
+
+!!! note
+    The package [ShiftedProximalOperators.jl](https://github.com/JuliaSmoothOptimizers/ShiftedProximalOperators.jl) mostly implements the shifted models `ψ`. 
+    For the unshifted version, these are often implemented in [ProximalOperators.jl](https://github.com/JuliaFirstOrder/ProximalOperators.jl) so that you might actually need to install the latter. For example, if you wish to use the L0 norm as a regularizer, then you should define `h` as `h = NormL0(1.0)` with [ProximalOperators.jl](https://github.com/JuliaFirstOrder/ProximalOperators.jl), you don't need to do anything else in this case because the shifted model of the L0 norm is already implemented in [ShiftedProximalOperators.jl](https://github.com/JuliaSmoothOptimizers/ShiftedProximalOperators.jl). 
+
+!!! warning 
+    The shifted model being proximable means that our solvers will not be able to automagically solve with any nonsmooth function that is given to it. Rather, the user is expected to provide an efficient solver for the proximal mapping.
+
+The following table shows which regularizers are readily available and which dependency is required to use the regularizer (the shifted model is always in `ShiftedProximalOperators.jl`).
+
+Regularizer | Shifted Model | Julia | Dependency
+------------|---------------|-------|-----------
+$\lambda ∥x∥_0$ | $\lambda ∥x + s∥_0$ | [`NormL0(λ)`](https://juliafirstorder.github.io/ProximalOperators.jl/stable/functions/#ProximalOperators.NormL0) | [ProximalOperators.jl](https://github.com/JuliaFirstOrder/ProximalOperators.jl)
+$\lambda ∥x∥_1$ | $\lambda ∥x + s∥_1$ | [`NormL1(λ)`](https://juliafirstorder.github.io/ProximalOperators.jl/stable/functions/#ProximalOperators.NormL1) | [ProximalOperators.jl](https://github.com/JuliaFirstOrder/ProximalOperators.jl)
+$\lambda ∥x∥_2$ | $\lambda ∥x + s∥_2$ | [`NormL2(λ)`](https://juliafirstorder.github.io/ProximalOperators.jl/stable/functions/#ProximalOperators.NormL2) | [ProximalOperators.jl](https://github.com/JuliaFirstOrder/ProximalOperators.jl)
+$\lambda ∥c(x)∥_2$ | $\lambda ∥c(x) + J(x)s∥_2$ | [`CompositeNormL2(λ)`](https://jso.dev/ShiftedProximalOperators.jl/dev/reference/#ShiftedProximalOperators.CompositeNormL2) | [ShiftedProximalOperators.jl](https://github.com/JuliaSmoothOptimizers/ShiftedProximalOperators.jl)
+
+---
+
+## Algorithms
+
+A presentation of each algorithm is given [here](@ref algorithms).
+
+---
+
+## Preallocating
+
+All solvers in RegularizedOptimization.jl have **in-place versions**.  
+Users can preallocate a workspace and reuse it across solves to avoid memory allocations, which is useful in repetitive scenarios.  
+
+---
+
+## How to Install
+
+RegularizedOptimization can be installed through the Julia package manager:
+
+```julia
+julia> ]
+pkg> add https://github.com/JuliaSmoothOptimizers/RegularizedOptimization.jl
+```
+
+---
+
+## Bug reports and discussions
+
+If you think you found a bug, please open an [issue](https://github.com/JuliaSmoothOptimizers/RegularizedOptimization.jl/issues).  
+Focused suggestions and requests can also be opened as issues. Before opening a pull request, we recommend starting an issue or a discussion first.  
+
+For general questions not suited for a bug report, feel free to start a discussion [here](https://github.com/JuliaSmoothOptimizers/Organization/discussions).  
+This forum is for questions and discussions about any of the [JuliaSmoothOptimizers](https://github.com/JuliaSmoothOptimizers) packages.  
diff --git a/docs/src/reference.md b/docs/src/reference.md
@@ -1,11 +1,5 @@
 # Reference
 
-## Contents
-
-```@contents
-Pages = ["reference.md"]
-```
-
 ## Index
 
 ```@index

diff --git a/docs/src/tutorial.md b/docs/src/tutorial.md