Fix some typos (#106)

Closes #103

Author: andreasnoack

src/eigenGeneral.jl

@@ -248,7 +248,7 @@ function doubleShiftQR!(
         Htmp22 = HH[istart+3, istart+1]
         HH[istart+3, istart+1] = 0
     else
-        # values doen't matter in this case but variables should be initialized
+        # values don't matter in this case but variables should be initialized
         Htmp21 = Htmp22 = Htmp11
     end
     G1, r = givens(

src/lapack.jl

@@ -670,7 +670,7 @@ for (f, elty) in ((:dtgevc_, :Float64), (:stgevc_, :Float32))
                 if mm != n
                     throw(
                         DimensionMismatch(
-                            "the number of columne in the output matrices are $mm, but should be $n",
+                            "the number of columns in the output matrices are $mm, but should be $n",
                         ),
                     )
                 end

src/svd.jl

@@ -572,7 +572,7 @@ A generic singular value decomposition (SVD). The implementation only uses Julia
 
 - `tol`: The relative tolerance for determining convergence. The default value is `eltype(T)` where `T` is the element type of the input matrix bidiagonal (i.e. after converting the matrix to bidiagonal form).
 
-- `full`: Sepcifies if all the left and right singular vectors be returned or if only the vectors us to the number of rows and columns of the input matrix `A` should be returned (the default).
+- `full`: If set to `true` then all the left and right singular vectors are returned. If set to `false` then only the vectors corresponding to the number of rows and columns of the input matrix `A` are returned (the default).
 
 - `debug`: A Boolean flag to activate debug information during the executions of the algorithm. The default is false.
 
@@ -613,7 +613,7 @@ function LinearAlgebra.svd!(
     # Convert the input matrix A to bidiagonal form and return a BidiagonalFactorization object
     BF = bidiagonalize!(A)
 
-    # The location of the super/sub-diagonal of the bidiagonal matrix depends on the aspect ratio of the input. For tall and square matrices, the bidiagonal matrix is upper whereas it is lower for wide input matrices as illustrated below. The 'O' entried indicate orthogonal (unitary) matrices.
+    # The location of the super/sub-diagonal of the bidiagonal matrix depends on the aspect ratio of the input. For tall and square matrices, the bidiagonal matrix is upper whereas it is lower for wide input matrices as illustrated below. The 'O' entries indicate orthogonal (unitary) matrices.
 
     #      A      =   Q_l  *   B   * Q_r^H
 

test/qr.jl

@@ -19,7 +19,7 @@ using GenericLinearAlgebra: qrBlocked!
         AqrQ = Aqr[Tuple{:QBlocked}]
         if m >= n
             @test (AqrQ'A)[1:min(m, n), :] ≈ Aqr[Tuple{:R}]
-        else # For type stbility getindex(,Tuple{:R}) throw when the output is trapezoid
+        else # For type stability getindex(,Tuple{:R}) throw when the output is trapezoid
             @test (AqrQ'A) ≈ triu(Aqr.factors)
         end
         @test AqrQ' * (AqrQ * A) ≈ A

test/rectfullpacked.jl

@@ -2,7 +2,7 @@ using Test
 import GenericLinearAlgebra
 import GenericLinearAlgebra: Ac_mul_A_RFP, TriangularRFP
 
-@testset "Rectuangular Full Pack Format" begin
+@testset "Rectangular Full Pack Format" begin
 
     @testset "Core generic functionality" for n in (6, 7), uplo in (:U, :L)