Merge branch 'main' into dk/trimul

docs/src/index.md

@@ -122,7 +122,7 @@ julia> R = sparsevec(I,V)
 
 The inverse of the [`sparse`](@ref) and [`sparsevec`](@ref) functions is
 [`findnz`](@ref), which retrieves the inputs used to create the sparse array.
-[`findall(!iszero, x)`](@ref) returns the cartesian indices of non-zero entries in `x`
+[`findall(!iszero, x)`](@ref) returns the Cartesian indices of non-zero entries in `x`
 (including stored entries equal to zero).
 
 ```jldoctest sparse_function

src/higherorderfns.jl

@@ -111,7 +111,7 @@ const SpBroadcasted2{Style<:SPVM,Axes,F,Args<:Tuple{SparseVecOrMat,SparseVecOrMa
 
 # (1) The definitions below provide a common interface to sparse vectors and matrices
 # sufficient for the purposes of map[!]/broadcast[!]. This interface treats sparse vectors
-# as n-by-one sparse matrices which, though technically incorrect, is how broacast[!] views
+# as n-by-one sparse matrices which, though technically incorrect, is how broadcast[!] views
 # sparse vectors in practice.
 @inline numrows(A::AbstractCompressedVector) = length(A)
 @inline numrows(A::AbstractSparseMatrixCSC) = size(A, 1)

src/linalg.jl

@@ -1492,7 +1492,7 @@ kron(a::_SparseKronGroup, b::Number) = a * b
 
 ## det, inv, cond
 
-inv(A::AbstractSparseMatrixCSC) = error("The inverse of a sparse matrix can often be dense and can cause the computer to run out of memory. If you are sure you have enough memory, please either convert your matrix to a dense matrix, e.g. by calling `Matrix` or if `A` can be factorized, use `\\` on the dense identity matrix, e.g. `A \\ Matrix{eltype(A)}(I, size(A)...)` restrictions of `\\` on sparse lhs applies. Altenatively, `A\\b` is generally preferable to `inv(A)*b`")
+inv(A::AbstractSparseMatrixCSC) = error("The inverse of a sparse matrix can often be dense and can cause the computer to run out of memory. If you are sure you have enough memory, please either convert your matrix to a dense matrix, e.g. by calling `Matrix` or if `A` can be factorized, use `\\` on the dense identity matrix, e.g. `A \\ Matrix{eltype(A)}(I, size(A)...)` restrictions of `\\` on sparse lhs applies. Alternatively, `A\\b` is generally preferable to `inv(A)*b`")
 
 # TODO
 

src/solvers/cholmod.jl

@@ -404,7 +404,7 @@ Factor(FC::FactorComponent) = FC.F
 #################
 
 # Dense wrappers
-# The ifelse here may be unecessary.
+# The ifelse here may be unnecessary.
 # nothing different actually occurs in cholmod_l_allocate vs cholmod_allocate AFAICT.
 # And CHOLMOD has no way of tracking the difference internally (no internal itype field).
 # This leads me to believe they can be mixed with long and int versions of sparse freely.

src/solvers/umfpack.jl

@@ -275,7 +275,7 @@ UmfpackWS(F::ATLU, refinement::Bool=has_refinement(F)) = UmfpackWS(F.parent, ref
 A shallow copy of UmfpackLU to use in multithreaded solve applications.
 This function duplicates the working space, control, info and lock fields.
 """
-# Not using simlar helps if the actual needed size has changed as it would need to be resized again
+# Not using similar helps if the actual needed size has changed as it would need to be resized again
 Base.copy(F::UmfpackLU{Tv, Ti}, ws=UmfpackWS(F)) where {Tv, Ti} =
     UmfpackLU(
         F.symbolic,
@@ -413,7 +413,7 @@ When `check = false`, responsibility for checking the decomposition's
 validity (via [`issuccess`](@ref)) lies with the user.
 
 The permutation `q` can either be a permutation vector or `nothing`. If no permutation vector
-is proveded or `q` is `nothing`, UMFPACK's default is used. If the permutation is not zero based, a
+is provided or `q` is `nothing`, UMFPACK's default is used. If the permutation is not zero based, a
 zero based copy is made.
 
 See also [`lu`](@ref)

test/sparsematrix_constructors_indexing.jl

@@ -1077,7 +1077,7 @@ end
         @test Base.isstored(A, c[1], c[2]) == false
     end
 
-    # `isstored` for adjoint and tranposed matrices:
+    # `isstored` for adjoint and transposed matrices:
     for trans in (adjoint, transpose)
         B = trans(A)
         stored_indices = [CartesianIndex(j, i) for (j, i) in zip(J, I)]
@@ -1390,7 +1390,7 @@ using Base: swaprows!, swapcols!
              (swaprows!, 2, 3), # Test swapping rows of unequal length
              (swaprows!, 2, 4), # Test swapping non-adjacent rows
              (swapcols!, 1, 2), # Test swapping columns where one column is fully sparse
-             (swapcols!, 2, 3), # Test swapping coulms of unequal length
+             (swapcols!, 2, 3), # Test swapping columns of unequal length
              (swapcols!, 2, 4)) # Test swapping non-adjacent columns
         Scopy = copy(S)
         Sdense = Array(S)