Documentation fixes (#68)

JuliaFirstOrder · Feb 20, 2022 · 9fe03c1 · 9fe03c1
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diff --git a/docs/src/guide/custom_objectives.jl b/docs/src/guide/custom_objectives.jl
@@ -1,3 +1,6 @@
+# ```@meta
+# CurrentModule = ProximalAlgorithms
+# ```
 # # [Custom objective terms](@id custom_terms)
 # 
 # ProximalAlgorithms relies on the first-order primitives defined in [ProximalCore](https://github.com/JuliaFirstOrder/ProximalCore.jl).
@@ -50,7 +53,7 @@ function ProximalCore.prox!(y, ::IndUnitBall, x, gamma)
     return zero(eltype(x))
 end
 
-# We can now minimize the function, for which we will use `PANOC`, which is a Newton-type method:
+# We can now minimize the function, for which we will use [`PANOC`](@ref), which is a Newton-type method:
 
 using ProximalAlgorithms
 

diff --git a/docs/src/guide/getting_started.jl b/docs/src/guide/getting_started.jl
@@ -1,3 +1,6 @@
+# ```@meta
+# CurrentModule = ProximalAlgorithms
+# ```
 # # Getting started
 #
 # The methods implemented in ProximalAlgorithms are commonly referred to as (you've guessed it) *proximal algorithms*,
@@ -40,7 +43,7 @@
 # 2. Call the algorithm on the problem description: this amounts to the initial point, the objective terms, and possibly additional required information (e.g. Lipschitz constants).
 # 
 # See [here](@ref problems_algorithms) for the list of available algorithm constructors, for different types of problems.
-# In general however, algorithms are instances of the [`IterativeAlgorithm`](@ref ProximalAlgorithms.IterativeAlgorithm) type.
+# In general however, algorithms are instances of the [`IterativeAlgorithm`](@ref) type.
 # 
 # ## [Example: box constrained quadratic](@id box_qp)
 # 
@@ -57,7 +60,7 @@ box_indicator = ProximalOperators.IndBox(0, 1)
 ffb = ProximalAlgorithms.FastForwardBackward(maxit=1000, tol=1e-5, verbose=true)
 
 # Here, we defined the cost function `quadratic_cost`, and the constraint indicator `box_indicator`.
-# Then we set up the optimization algorithm of choice, `FastForwardBackward`,
+# Then we set up the optimization algorithm of choice, [`FastForwardBackward`](@ref),
 # with options for the maximum number of iterations, termination tolerance, verbosity.
 # Finally, we run the algorithm by providing an initial point and the objective terms defining the problem:
 
@@ -109,7 +112,7 @@ scatter!([solution[1]], [solution[2]], color=:red, markershape=:star5, label="co
 # ## [Example: box constrained quadratic (cont)](@id box_qp_cont)
 # 
 # Let's solve the problem from the [previous example](@ref box_qp) by directly interacting with the underlying iterator: 
-# the `FastForwardBackward` algorithm internally uses a [`FastForwardBackwardIteration`](@ref ProximalAlgorithms.FastForwardBackwardIteration) object.
+# the `FastForwardBackward` algorithm internally uses a [`FastForwardBackwardIteration`](@ref) object.
 
 ffbiter = ProximalAlgorithms.FastForwardBackwardIteration(x0=ones(2), f=quadratic_cost, g=box_indicator)
 

diff --git a/docs/src/guide/implemented_algorithms.md b/docs/src/guide/implemented_algorithms.md
@@ -1,3 +1,6 @@
+```@meta
+CurrentModule = ProximalAlgorithms
+```
 # [Problem types and algorithms](@id problems_algorithms)
 
 !!! warning
@@ -21,13 +24,13 @@ This is the most popular model, by far the most thoroughly studied, and an abund
 
 Algorithm | Assumptions | Oracle | Implementation | References
 ----------|-------------|--------|----------------|-----------
-Proximal gradient | ``f`` smooth | ``\nabla f``, ``\operatorname{prox}_{\gamma g}`` | [`ForwardBackward`](@ref ProximalAlgorithms.ForwardBackward) | [Lions1979](@cite)
-Douglas-Rachford | | ``\operatorname{prox}_{\gamma f}``, ``\operatorname{prox}_{\gamma g}`` | [`DouglasRachford`](@ref ProximalAlgorithms.DouglasRachford) | [Eckstein1992](@cite)
-Fast proximal gradient | ``f`` convex, smooth, ``g`` convex | ``\nabla f``, ``\operatorname{prox}_{\gamma g}`` | [`FastForwardBackward`](@ref ProximalAlgorithms.FastForwardBackward) | [Tseng2008](@cite), [Beck2009](@cite)
-PANOC | ``f`` smooth | ``\nabla f``, ``\operatorname{prox}_{\gamma g}`` | [`PANOC`](@ref ProximalAlgorithms.PANOC) | [Stella2017](@cite)
-ZeroFPR | ``f`` smooth | ``\nabla f``, ``\operatorname{prox}_{\gamma g}`` | [`ZeroFPR`](@ref ProximalAlgorithms.ZeroFPR) | [Themelis2018](@cite)
-Douglas-Rachford line-search | ``f`` smooth | ``\operatorname{prox}_{\gamma f}``, ``\operatorname{prox}_{\gamma g}`` | [`DRLS`](@ref ProximalAlgorithms.DRLS) | [Themelis2020](@cite)
-PANOC+ | ``f`` locally smooth | ``\nabla f``, ``\operatorname{prox}_{\gamma g}`` | [`PANOCplus`](@ref ProximalAlgorithms.PANOCplus) | [DeMarchi2021](@cite)
+Proximal gradient | ``f`` smooth | ``\nabla f``, ``\operatorname{prox}_{\gamma g}`` | [`ForwardBackward`](@ref) | [Lions1979](@cite)
+Douglas-Rachford | | ``\operatorname{prox}_{\gamma f}``, ``\operatorname{prox}_{\gamma g}`` | [`DouglasRachford`](@ref) | [Eckstein1992](@cite)
+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)
 
 ```@docs
 ProximalAlgorithms.ForwardBackward
@@ -54,7 +57,7 @@ Therefore, ad-hoc iteration schemes have been studied.
 
 Algorithm | Assumptions | Oracle | Implementation | References
 ----------|-------------|--------|----------------|-----------
-Davis-Yin | ``f`` convex and smooth, ``g, h`` convex | ``\nabla f``, ``\operatorname{prox}_{\gamma g}``, ``\operatorname{prox}_{\gamma h}`` | [`DavisYin`](@ref ProximalAlgorithms.DavisYin) | [Davis2017](@cite) 
+Davis-Yin | ``f`` convex and smooth, ``g, h`` convex | ``\nabla f``, ``\operatorname{prox}_{\gamma g}``, ``\operatorname{prox}_{\gamma h}`` | [`DavisYin`](@ref) | [Davis2017](@cite) 
 
 ```@docs
 ProximalAlgorithms.DavisYin
@@ -68,9 +71,9 @@ For this reason, specific algorithms by the name of "primal-dual" splitting sche
 
 Algorithm | Assumptions | Oracle | Implementation | References
 ----------|-------------|--------|----------------|-----------
-Chambolle-Pock | ``f\equiv 0``, ``g, h`` convex, ``L`` linear operator | ``\operatorname{prox}_{\gamma g}``, ``\operatorname{prox}_{\gamma h}``, ``L``, ``L^*`` | [`ChambollePock`](@ref ProximalAlgorithms.ChambollePock) | [Chambolle2011](@cite)
-Vu-Condat | ``f`` convex and smooth, ``g, h`` convex, ``L`` linear operator | ``\nabla f``, ``\operatorname{prox}_{\gamma g}``, ``\operatorname{prox}_{\gamma h}``, ``L``, ``L^*`` | [`VuCodat`](@ref ProximalAlgorithms.VuCondat) | [Vu2013](@cite), [Condat2013](@cite)
-AFBA      | ``f`` convex and smooth, ``g, h`` convex, ``L`` linear operator | ``\nabla f``, ``\operatorname{prox}_{\gamma g}``, ``\operatorname{prox}_{\gamma h}``, ``L``, ``L^*`` | [`AFBA`](@ref ProximalAlgorithms.AFBA) | [Latafat2017](@cite)
+Chambolle-Pock | ``f\equiv 0``, ``g, h`` convex, ``L`` linear operator | ``\operatorname{prox}_{\gamma g}``, ``\operatorname{prox}_{\gamma h}``, ``L``, ``L^*`` | [`ChambollePock`](@ref) | [Chambolle2011](@cite)
+Vu-Condat | ``f`` convex and smooth, ``g, h`` convex, ``L`` linear operator | ``\nabla f``, ``\operatorname{prox}_{\gamma g}``, ``\operatorname{prox}_{\gamma h}``, ``L``, ``L^*`` | [`VuCodat`](@ref) | [Vu2013](@cite), [Condat2013](@cite)
+AFBA      | ``f`` convex and smooth, ``g, h`` convex, ``L`` linear operator | ``\nabla f``, ``\operatorname{prox}_{\gamma g}``, ``\operatorname{prox}_{\gamma h}``, ``L``, ``L^*`` | [`AFBA`](@ref) | [Latafat2017](@cite)
 
 ```@docs
 ProximalAlgorithms.ChambollePock