Fix a few typos.

Author: kellertuer

src/documentation_glossary.jl

@@ -427,16 +427,16 @@ define!(
     :Variable,
     :grad_f,
     :description,
-    (; M = "M", p = "p") ->
-    "the (Riemannian) gradient ``$(_tex(:grad))f: $(_math(:M, M = M)) → $(_math(:TpM; M = M, p = p))`` of f as a function `(M, p) -> X` or a function `(M, X, p) -> X` computing `X` in-place",
+    (; M = "M", p = "p", f="f", kwargs...) ->
+    "the (Riemannian) gradient ``$(_tex(:grad))$f: $(_math(:M, M = M)) → $(_math(:TpM; M = M, p = p))`` of $f as a function `(M, p) -> X` or a function `(M, X, p) -> X` computing `X` in-place",
 )
 
 define!(
     :Variable,
     :Hess_f,
     :description,
-    (; M = "M", p = "p") ->
-    "the (Riemannian) Hessian ``$(_tex(:Hess))f: $(_math(:TpM, M = M, p = p)) → $(_math(:TpM; M = M, p = p))`` of f as a function `(M, p, X) -> Y` or a function `(M, Y, p, X) -> Y` computing `Y` in-place",
+    (; M = "M", p = "p", f="f") ->
+    "the (Riemannian) Hessian ``$(_tex(:Hess))$f: $(_math(:TpM, M = M, p = p)) → $(_math(:TpM; M = M, p = p))`` of $f as a function `(M, p, X) -> Y` or a function `(M, Y, p, X) -> Y` computing `Y` in-place",
 )
 
 define!(
@@ -542,19 +542,10 @@ define!(
     :Variable,
     :subgrad_f,
     :description,
-    (; M = "M", p = "p") -> """
-    the subgradient ``∂f: $(_math(:M; M = M)) → $(_math(:TM; M = M))`` of f as a function `(M, p) -> X`
-    or a function `(M, X, p) -> X` computing `X` in-place.
-    This function should always only return one element from the subgradient.
+    (; M = "M", p = "p", f="f", kwargs...) -> """
+    the subgradient ``∂$f: $(_math(:M; M = M)) → $(_math(:TM; M = M))`` of ``$f`` as a function `(M, p) -> X` or a function `(M, X, p) -> X` computing `X` in-place. This function should always only return one element from the subgradient.
     """,
 )
-define!(
-    :Variable,
-    :subgrad_f,
-    :description,
-    (; M = "M") ->
-    " a state to specify the sub solver to use. For a closed form solution, this indicates the type of function.",
-)
 
 define!(
     :Variable,

src/solvers/DouglasRachford.jl

@@ -147,8 +147,8 @@ _doc_Douglas_Rachford = """
     DouglasRachford!(M, f, proxes_f, p)
     DouglasRachford!(M, mpo, p)
 
-Compute the Douglas-Rachford algorithm on the manifold ``$(_math(:M))``, starting from `p``
-given the (two) proximal maps `proxes_f`, see [ BergmannPerschSteidl:2016](@cite).
+Compute the Douglas-Rachford algorithm on the manifold ``$(_math(:M))``, starting from `p`
+given the (two) proximal maps `proxes_f`, see [BergmannPerschSteidl:2016](@cite).
 
 For ``k>2`` proximal maps, the problem is reformulated using the parallel Douglas Rachford:
 a vectorial proximal map on the power manifold ``$(_math(:M))^k`` is introduced as the first
@@ -157,10 +157,9 @@ This hence also boils down to two proximal maps, though each evaluates proximal
 that is, component wise in a vector.
 
 !!! note
-
-   The parallel Douglas Rachford does not work in-place for now, since
-   while creating the new staring point `p'` on the power manifold, a copy of `p`
-   Is created
+    The parallel Douglas Rachford does not work in-place for now, since
+    while creating the new staring point `p'` on the power manifold, a copy of `p`
+    Is created
 
 If you provide a [`ManifoldProximalMapObjective`](@ref) `mpo` instead, the proximal maps are kept unchanged.
 

src/solvers/difference_of_convex_algorithm.jl

@@ -131,6 +131,14 @@ Then repeat for ``k=0,1,…``
 
 until the stopping criterion (see the `stopping_criterion` keyword is fulfilled.
 
+# Input
+
+$(_var(:Argument, :M; type = true))
+$(_var(:Argument, :f; add = "total cost function `f = g - h`"))
+* `g`: the smooth part ``g``of the cost function
+$(_var(:Argument, :subgrad_f, "∂h"; f="h"))
+$(_var(:Argument, :p))
+
 # Keyword arguments
 
 $(_var(:Keyword, :evaluation))

src/solvers/difference_of_convex_proximal_point.jl

@@ -186,6 +186,12 @@ See [AlmeidaNetoOliveiraSouza:2020](@cite) for more details on the modified vari
 where steps 4-6 are slightly changed, since here the classical proximal point method for
 DC functions is obtained for ``s_k = 1`` and one can hence employ usual line search method.
 
+# Input
+
+$(_var(:Argument, :M; type = true))
+$(_var(:Argument, :f; add = "total cost function `f = g - h`"))
+$(_var(:Argument, :grad_f, "grad_h"; f="h"))
+$(_var(:Argument, :p))
 
 # Keyword arguments
 

src/solvers/proximal_gradient_method.jl

@@ -35,6 +35,7 @@ $(_var(:Argument, :M; type = true))
 $(_var(:Argument, :f; add = "total cost function `f = g + h`"))
 * `g`:              the smooth part of the cost function
 * `grad_g`:           a gradient `(M,p) -> X` or `(M, X, p) -> X` of the smooth part ``g`` of the problem
+$(_var(:Argument, :p))
 
 # Keyword Arguments
 

src/solvers/subgradient.jl

@@ -102,7 +102,7 @@ For more details see [FerreiraOliveira:1998](@cite).
 
 $(_var(:Argument, :M; type = true))
 $(_var(:Argument, :f))
-* `∂f`: the (sub)gradient ``∂ f: $(_math(:M)) → T$(_math(:M))`` of f
+* `∂f`: the (sub)gradient ``∂ f: $(_math(:M)) → T$(_math(:M))`` of ``f``
 $(_var(:Argument, :p))
 
 alternatively to `f` and `∂f` a [`ManifoldSubgradientObjective`](@ref) `sgo` can be provided.