Use Conjugate Gradient (no plural) as full name of CG

docs/make.jl

@@ -16,7 +16,7 @@ makedocs(
 		"Getting started" => "getting_started.md",
 		"Preconditioning" => "preconditioning.md",
 		"Linear systems" => [
-			"Conjugate Gradients" => "linear_systems/cg.md",
+			"Conjugate Gradient" => "linear_systems/cg.md",
 			"Chebyshev iteration" => "linear_systems/chebyshev.md",
 			"MINRES" => "linear_systems/minres.md",
 			"BiCGStab(l)" => "linear_systems/bicgstabl.md",

docs/src/index.md

@@ -12,13 +12,13 @@ When solving linear systems $Ax = b$ for a square matrix $A$ there are quite som
 
 | Method              | When to use it                                                           |
 |---------------------|--------------------------------------------------------------------------|
-| [Conjugate Gradients](@ref CG) | Best choice for **symmetric**, **positive-definite** matrices |
+| [Conjugate Gradient](@ref CG) | Best choice for **symmetric**, **positive-definite** matrices |
 | [MINRES](@ref MINRES) | For **symmetric**, **indefinite** matrices |
 | [GMRES](@ref GMRES) | For **nonsymmetric** matrices when a good [preconditioner](@ref Preconditioning) is available |
 | [IDR(s)](@ref IDRs) | For **nonsymmetric**, **strongly indefinite** problems without a good preconditioner |
 | [BiCGStab(l)](@ref BiCGStabl) | Otherwise for **nonsymmetric** problems |
 
-We also offer [Chebyshev iteration](@ref Chebyshev) as an alternative to Conjugate Gradients when bounds on the spectrum are known.
+We also offer [Chebyshev iteration](@ref Chebyshev) as an alternative to Conjugate Gradient when bounds on the spectrum are known.
 
 Stationary methods like [Jacobi](@ref), [Gauss-Seidel](@ref), [SOR](@ref) and [SSOR](@ref) can be used as smoothers to reduce high-frequency components in the error in just a few iterations.
 

docs/src/linear_systems/cg.md

@@ -1,6 +1,6 @@
-# [Conjugate Gradients (CG)](@id CG)
+# [Conjugate Gradient (CG)](@id CG)
 
-Conjugate Gradients solves $Ax = b$ approximately for $x$ where $A$ is a symmetric, positive-definite linear operator and $b$ the right-hand side vector. The method uses short recurrences and therefore has fixed memory costs and fixed computational costs per iteration.
+Conjugate Gradient solves $Ax = b$ approximately for $x$ where $A$ is a symmetric, positive-definite linear operator and $b$ the right-hand side vector. The method uses short recurrences and therefore has fixed memory costs and fixed computational costs per iteration.
 
 ## Usage
 

docs/src/linear_systems/chebyshev.md

@@ -2,7 +2,7 @@
 
 Chebyshev iteration solves the problem $Ax=b$ approximately for $x$ where $A$ is a symmetric, definite linear operator and $b$ the right-hand side vector. The methods assumes the interval $[\lambda_{min}, \lambda_{max}]$ containing all eigenvalues of $A$ is known, so that $x$ can be iteratively constructed via a Chebyshev polynomial with zeros in this interval. This polynomial ultimately acts as a filter that removes components in the direction of the eigenvectors from the initial residual.
 
-The main advantage with respect to Conjugate Gradients is that BLAS1 operations such as inner products are avoided.
+The main advantage with respect to Conjugate Gradient is that BLAS1 operations such as inner products are avoided.
 
 ## Usage
 

test/cg.jl

@@ -17,7 +17,7 @@ end
 
 ldiv!(y, P::JacobiPrec, x) = y .= x ./ P.diagonal
 
-@testset "Conjugate Gradients" begin
+@testset "Conjugate Gradient" begin
 
 Random.seed!(1234321)
 

test/runtests.jl

@@ -9,7 +9,7 @@ include("hessenberg.jl")
 #Stationary solvers
 include("stationary.jl")
 
-#Conjugate gradients
+#Conjugate gradient
 include("cg.jl")
 
 #BiCGStab(l)