Sparse Arrays

Julia has support for sparse vectors and sparse matrices in the SparseArrays stdlib module. Sparse arrays are arrays that contain enough zeros that storing them in a special data structure leads to savings in space and execution time, compared to dense arrays.

External packages which implement different sparse storage types, multidimensional sparse arrays, and more can be found in Noteworthy External Sparse Packages

Compressed Sparse Column (CSC) Sparse Matrix Storage

In Julia, sparse matrices are stored in the Compressed Sparse Column (CSC) format. Julia sparse matrices have the type SparseMatrixCSC{Tv,Ti}, where Tv is the type of the stored values, and Ti is the integer type for storing column pointers and row indices. The internal representation of SparseMatrixCSC is as follows:

struct SparseMatrixCSC{Tv,Ti<:Integer} <: AbstractSparseMatrixCSC{Tv,Ti}
    m::Int                  # Number of rows
    n::Int                  # Number of columns
    colptr::Vector{Ti}      # Column j is in colptr[j]:(colptr[j+1]-1)
    rowval::Vector{Ti}      # Row indices of stored values
    nzval::Vector{Tv}       # Stored values, typically nonzeros
end

The compressed sparse column storage makes it easy and quick to access the elements in the column of a sparse matrix, whereas accessing the sparse matrix by rows is considerably slower. Operations such as insertion of previously unstored entries one at a time in the CSC structure tend to be slow. This is because all elements of the sparse matrix that are beyond the point of insertion have to be moved one place over.

All operations on sparse matrices are carefully implemented to exploit the CSC data structure for performance, and to avoid expensive operations.

If you have data in CSC format from a different application or library, and wish to import it in Julia, make sure that you use 1-based indexing. The row indices in every column need to be sorted, and if they are not, the matrix will display incorrectly. If your SparseMatrixCSC object contains unsorted row indices, one quick way to sort them is by doing a double transpose. Since the transpose operation is lazy, make a copy to materialize each transpose.

In some applications, it is convenient to store explicit zero values in a SparseMatrixCSC. These are accepted by functions in Base (but there is no guarantee that they will be preserved in mutating operations). Such explicitly stored zeros are treated as structural nonzeros by many routines. The nnz function returns the number of elements explicitly stored in the sparse data structure, including non-structural zeros. In order to count the exact number of numerical nonzeros, use count(!iszero, x), which inspects every stored element of a sparse matrix. dropzeros, and the in-place dropzeros!, can be used to remove stored zeros from the sparse matrix.

julia> A = sparse([1, 1, 2, 3], [1, 3, 2, 3], [0, 1, 2, 0])
3×3 SparseMatrixCSC{Int64, Int64} with 4 stored entries:
 0  ⋅  1
 ⋅  2  ⋅
 ⋅  ⋅  0

julia> dropzeros(A)
3×3 SparseMatrixCSC{Int64, Int64} with 2 stored entries:
 ⋅  ⋅  1
 ⋅  2  ⋅
 ⋅  ⋅  ⋅

Sparse Vector Storage

Sparse vectors are stored in a close analog to compressed sparse column format for sparse matrices. In Julia, sparse vectors have the type SparseVector{Tv,Ti} where Tv is the type of the stored values and Ti the integer type for the indices. The internal representation is as follows:

struct SparseVector{Tv,Ti<:Integer} <: AbstractSparseVector{Tv,Ti}
    n::Int              # Length of the sparse vector
    nzind::Vector{Ti}   # Indices of stored values
    nzval::Vector{Tv}   # Stored values, typically nonzeros
end

As for SparseMatrixCSC, the SparseVector type can also contain explicitly stored zeros. (See Sparse Matrix Storage.).

Sparse Vector and Matrix Constructors

The simplest way to create a sparse array is to use a function equivalent to the zeros function that Julia provides for working with dense arrays. To produce a sparse array instead, you can use the same name with an sp prefix:

julia> spzeros(3)
3-element SparseVector{Float64, Int64} with 0 stored entries

The sparse function is often a handy way to construct sparse arrays. For example, to construct a sparse matrix we can input a vector I of row indices, a vector J of column indices, and a vector V of stored values (this is also known as the COO (coordinate) format). sparse(I,J,V) then constructs a sparse matrix such that S[I[k], J[k]] = V[k]. The equivalent sparse vector constructor is sparsevec, which takes the (row) index vector I and the vector V with the stored values and constructs a sparse vector R such that R[I[k]] = V[k].

julia> I = [1, 4, 3, 5]; J = [4, 7, 18, 9]; V = [1, 2, -5, 3];

julia> S = sparse(I,J,V)
5×18 SparseMatrixCSC{Int64, Int64} with 4 stored entries:
⎡⠀⠈⠀⠀⠀⠀⠀⠀⢀⎤
⎣⠀⠀⠀⠂⡀⠀⠀⠀⠀⎦

julia> R = sparsevec(I,V)
5-element SparseVector{Int64, Int64} with 4 stored entries:
  [1]  =  1
  [3]  =  -5
  [4]  =  2
  [5]  =  3

The inverse of the sparse and sparsevec functions is findnz, which retrieves the inputs used to create the sparse array (including stored entries equal to zero). findall(!iszero, x) returns the Cartesian indices of non-zero entries in x (not including stored entries equal to zero).

julia> findnz(S)
([1, 4, 5, 3], [4, 7, 9, 18], [1, 2, 3, -5])

julia> findall(!iszero, S)
4-element Vector{CartesianIndex{2}}:
 CartesianIndex(1, 4)
 CartesianIndex(4, 7)
 CartesianIndex(5, 9)
 CartesianIndex(3, 18)

julia> findnz(R)
([1, 3, 4, 5], [1, -5, 2, 3])

julia> findall(!iszero, R)
4-element Vector{Int64}:
 1
 3
 4
 5

Another way to create a sparse array is to convert a dense array into a sparse array using the sparse function:

julia> sparse(Matrix(1.0I, 5, 5))
5×5 SparseMatrixCSC{Float64, Int64} with 5 stored entries:
 1.0   ⋅    ⋅    ⋅    ⋅
  ⋅   1.0   ⋅    ⋅    ⋅
  ⋅    ⋅   1.0   ⋅    ⋅
  ⋅    ⋅    ⋅   1.0   ⋅
  ⋅    ⋅    ⋅    ⋅   1.0

julia> sparse([1.0, 0.0, 1.0])
3-element SparseVector{Float64, Int64} with 2 stored entries:
  [1]  =  1.0
  [3]  =  1.0

You can go in the other direction using the Array constructor. The issparse function can be used to query if a matrix is sparse.

julia> issparse(spzeros(5))
true

Sparse matrix operations

Arithmetic operations on sparse matrices also work as they do on dense matrices. Indexing of, assignment into, and concatenation of sparse matrices work in the same way as dense matrices. Indexing operations, especially assignment, are expensive, when carried out one element at a time. In many cases it may be better to convert the sparse matrix into (I,J,V) format using findnz, manipulate the values or the structure in the dense vectors (I,J,V), and then reconstruct the sparse matrix.

Correspondence of dense and sparse methods

The following table gives a correspondence between built-in methods on sparse matrices and their corresponding methods on dense matrix types. In general, methods that generate sparse matrices differ from their dense counterparts in that the resulting matrix follows the same sparsity pattern as a given sparse matrix S, or that the resulting sparse matrix has density d, i.e. each matrix element has a probability d of being non-zero.

Details can be found in the Sparse Vectors and Matrices section of the standard library reference.

Sparse	Dense	Description
`spzeros(m,n)`	`zeros(m,n)`	Creates a m-by-n matrix of zeros. (`spzeros(m,n)` is empty.)
`sparse(I,n,n)`	`Matrix(I,n,n)`	Creates a n-by-n identity matrix.
`sparse(A)`	`Array(S)`	Interconverts between dense and sparse formats.
`sprand(m,n,d)`	`rand(m,n)`	Creates a m-by-n random matrix (of density d) with iid non-zero elements distributed uniformly on the half-open interval $[0, 1)$.
`sprandn(m,n,d)`	`randn(m,n)`	Creates a m-by-n random matrix (of density d) with iid non-zero elements distributed according to the standard normal (Gaussian) distribution.
`sprandn(rng,m,n,d)`	`randn(rng,m,n)`	Creates a m-by-n random matrix (of density d) with iid non-zero elements generated with the `rng` random number generator

SparseArrays API

SparseArrays.AbstractSparseArray — Type

AbstractSparseArray{Tv,Ti,N}

Supertype for N-dimensional sparse arrays (or array-like types) with elements of type Tv and index type Ti. SparseMatrixCSC, SparseVector and SuiteSparse.CHOLMOD.Sparse are subtypes of this.

SparseArrays.AbstractSparseVector — Type

AbstractSparseVector{Tv,Ti}

Supertype for one-dimensional sparse arrays (or array-like types) with elements of type Tv and index type Ti. Alias for AbstractSparseArray{Tv,Ti,1}.

SparseArrays.AbstractSparseMatrix — Type

AbstractSparseMatrix{Tv,Ti}

Supertype for two-dimensional sparse arrays (or array-like types) with elements of type Tv and index type Ti. Alias for AbstractSparseArray{Tv,Ti,2}.

SparseArrays.SparseVector — Type

SparseVector{Tv,Ti<:Integer} <: AbstractSparseVector{Tv,Ti}

Vector type for storing sparse vectors. Can be created by passing the length of the vector, a sorted vector of non-zero indices, and a vector of non-zero values.

For instance, the vector [5, 6, 0, 7] can be represented as

SparseVector(4, [1, 2, 4], [5, 6, 7])

This indicates that the element at index 1 is 5, at index 2 is 6, at index 3 is zero(Int), and at index 4 is 7.

It may be more convenient to create sparse vectors directly from dense vectors using sparse as

sparse([5, 6, 0, 7])

yields the same sparse vector.

SparseArrays.SparseMatrixCSC — Type

SparseMatrixCSC{Tv,Ti<:Integer} <: AbstractSparseMatrixCSC{Tv,Ti}

Matrix type for storing sparse matrices in the Compressed Sparse Column format. The standard way of constructing SparseMatrixCSC is through the sparse function. See also spzeros, spdiagm and sprand.

SparseArrays.sparse — Function

sparse(A)

Convert an AbstractMatrix A into a sparse matrix.

Examples

julia> A = Matrix(1.0I, 3, 3)
3×3 Matrix{Float64}:
 1.0  0.0  0.0
 0.0  1.0  0.0
 0.0  0.0  1.0

julia> sparse(A)
3×3 SparseMatrixCSC{Float64, Int64} with 3 stored entries:
 1.0   ⋅    ⋅
  ⋅   1.0   ⋅
  ⋅    ⋅   1.0

sparse(I, J, V,[ m, n, combine])

Create a sparse matrix S of dimensions m x n such that S[I[k], J[k]] = V[k]. The combine function is used to combine duplicates. If m and n are not specified, they are set to maximum(I) and maximum(J) respectively. If the combine function is not supplied, combine defaults to + unless the elements of V are Booleans in which case combine defaults to |. All elements of I must satisfy 1 <= I[k] <= m, and all elements of J must satisfy 1 <= J[k] <= n. Numerical zeros in (I, J, V) are retained as structural nonzeros; to drop numerical zeros, use dropzeros!.

For additional documentation and an expert driver, see SparseArrays.sparse!.

Examples

julia> Is = [1; 2; 3];

julia> Js = [1; 2; 3];

julia> Vs = [1; 2; 3];

julia> sparse(Is, Js, Vs)
3×3 SparseMatrixCSC{Int64, Int64} with 3 stored entries:
 1  ⋅  ⋅
 ⋅  2  ⋅
 ⋅  ⋅  3

SparseArrays.sparse! — Function

sparse!(I::AbstractVector{Ti}, J::AbstractVector{Ti}, V::AbstractVector{Tv},
        m::Integer, n::Integer, combine, klasttouch::Vector{Ti},
        csrrowptr::Vector{Ti}, csrcolval::Vector{Ti}, csrnzval::Vector{Tv},
        [csccolptr::Vector{Ti}], [cscrowval::Vector{Ti}, cscnzval::Vector{Tv}] ) where {Tv,Ti<:Integer}

Parent of and expert driver for sparse; see sparse for basic usage. This method allows the user to provide preallocated storage for sparse's intermediate objects and result as described below. This capability enables more efficient successive construction of SparseMatrixCSCs from coordinate representations, and also enables extraction of an unsorted-column representation of the result's transpose at no additional cost.

This method consists of three major steps: (1) Counting-sort the provided coordinate representation into an unsorted-row CSR form including repeated entries. (2) Sweep through the CSR form, simultaneously calculating the desired CSC form's column-pointer array, detecting repeated entries, and repacking the CSR form with repeated entries combined; this stage yields an unsorted-row CSR form with no repeated entries. (3) Counting-sort the preceding CSR form into a fully-sorted CSC form with no repeated entries.

Input arrays csrrowptr, csrcolval, and csrnzval constitute storage for the intermediate CSR forms and require length(csrrowptr) >= m + 1, length(csrcolval) >= length(I), and length(csrnzval >= length(I)). Input array klasttouch, workspace for the second stage, requires length(klasttouch) >= n. Optional input arrays csccolptr, cscrowval, and cscnzval constitute storage for the returned CSC form S. If necessary, these are resized automatically to satisfy length(csccolptr) = n + 1, length(cscrowval) = nnz(S) and length(cscnzval) = nnz(S); hence, if nnz(S) is unknown at the outset, passing in empty vectors of the appropriate type (Vector{Ti}() and Vector{Tv}() respectively) suffices, or calling the sparse! method neglecting cscrowval and cscnzval.

On return, csrrowptr, csrcolval, and csrnzval contain an unsorted-column representation of the result's transpose.

You may reuse the input arrays' storage (I, J, V) for the output arrays (csccolptr, cscrowval, cscnzval). For example, you may call sparse!(I, J, V, csrrowptr, csrcolval, csrnzval, I, J, V). Note that they will be resized to satisfy the conditions above.

For the sake of efficiency, this method performs no argument checking beyond 1 <= I[k] <= m and 1 <= J[k] <= n. Use with care. Testing with --check-bounds=yes is wise.

This method runs in O(m, n, length(I)) time. The HALFPERM algorithm described in F. Gustavson, "Two fast algorithms for sparse matrices: multiplication and permuted transposition," ACM TOMS 4(3), 250-269 (1978) inspired this method's use of a pair of counting sorts.

SparseArrays.sparse!(I, J, V, [m, n, combine]) -> SparseMatrixCSC

Variant of sparse! that re-uses the input vectors (I, J, V) for the final matrix storage. After construction the input vectors will alias the matrix buffers; S.colptr === I, S.rowval === J, and S.nzval === V holds, and they will be resize!d as necessary.

Note that some work buffers will still be allocated. Specifically, this method is a convenience wrapper around sparse!(I, J, V, m, n, combine, klasttouch, csrrowptr, csrcolval, csrnzval, csccolptr, cscrowval, cscnzval) where this method allocates klasttouch, csrrowptr, csrcolval, and csrnzval of appropriate size, but reuses I, J, and V for csccolptr, cscrowval, and cscnzval.

Arguments m, n, and combine defaults to maximum(I), maximum(J), and +, respectively.

Julia 1.10

This method requires Julia version 1.10 or later.

SparseArrays.sparsevec — Function

sparsevec(I, V, [m, combine])

Create a sparse vector S of length m such that S[I[k]] = V[k]. Duplicates are combined using the combine function, which defaults to + if no combine argument is provided, unless the elements of V are Booleans in which case combine defaults to |.

Examples

julia> II = [1, 3, 3, 5]; V = [0.1, 0.2, 0.3, 0.2];

julia> sparsevec(II, V)
5-element SparseVector{Float64, Int64} with 3 stored entries:
  [1]  =  0.1
  [3]  =  0.5
  [5]  =  0.2

julia> sparsevec(II, V, 8, -)
8-element SparseVector{Float64, Int64} with 3 stored entries:
  [1]  =  0.1
  [3]  =  -0.1
  [5]  =  0.2

julia> sparsevec([1, 3, 1, 2, 2], [true, true, false, false, false])
3-element SparseVector{Bool, Int64} with 3 stored entries:
  [1]  =  1
  [2]  =  0
  [3]  =  1

sparsevec(d::Dict, [m])

Create a sparse vector of length m where the nonzero indices are keys from the dictionary, and the nonzero values are the values from the dictionary.

Examples

julia> sparsevec(Dict(1 => 3, 2 => 2))
2-element SparseVector{Int64, Int64} with 2 stored entries:
  [1]  =  3
  [2]  =  2

sparsevec(A)

Convert a vector A into a sparse vector of length m.

Examples

julia> sparsevec([1.0, 2.0, 0.0, 0.0, 3.0, 0.0])
6-element SparseVector{Float64, Int64} with 3 stored entries:
  [1]  =  1.0
  [2]  =  2.0
  [5]  =  3.0

Base.similar — Method

similar(A::AbstractSparseMatrixCSC{Tv,Ti}, [::Type{TvNew}, ::Type{TiNew}, m::Integer, n::Integer]) where {Tv,Ti}

Create an uninitialized mutable array with the given element type, index type, and size, based upon the given source SparseMatrixCSC. The new sparse matrix maintains the structure of the original sparse matrix, except in the case where dimensions of the output matrix are different from the output.

The output matrix has zeros in the same locations as the input, but uninitialized values for the nonzero locations.

SparseArrays.issparse — Function

issparse(S)

Returns true if S is sparse, and false otherwise.

Examples

julia> sv = sparsevec([1, 4], [2.3, 2.2], 10)
10-element SparseVector{Float64, Int64} with 2 stored entries:
  [1]  =  2.3
  [4]  =  2.2

julia> issparse(sv)
true

julia> issparse(Array(sv))
false

SparseArrays.nnz — Function

nnz(A)

Returns the number of stored (filled) elements in a sparse array.

Examples

julia> A = sparse(2I, 3, 3)
3×3 SparseMatrixCSC{Int64, Int64} with 3 stored entries:
 2  ⋅  ⋅
 ⋅  2  ⋅
 ⋅  ⋅  2

julia> nnz(A)
3

SparseArrays.findnz — Function

findnz(A::SparseMatrixCSC)

Return a tuple (I, J, V) where I and J are the row and column indices of the stored ("structurally non-zero") values in sparse matrix A, and V is a vector of the values.

Examples

julia> A = sparse([1 2 0; 0 0 3; 0 4 0])
3×3 SparseMatrixCSC{Int64, Int64} with 4 stored entries:
 1  2  ⋅
 ⋅  ⋅  3
 ⋅  4  ⋅

julia> findnz(A)
([1, 1, 3, 2], [1, 2, 2, 3], [1, 2, 4, 3])

SparseArrays.spzeros — Function

spzeros([type,]m[,n])

Create a sparse vector of length m or sparse matrix of size m x n. This sparse array will not contain any nonzero values. No storage will be allocated for nonzero values during construction. The type defaults to Float64 if not specified.

Examples

julia> spzeros(3, 3)
3×3 SparseMatrixCSC{Float64, Int64} with 0 stored entries:
  ⋅    ⋅    ⋅
  ⋅    ⋅    ⋅
  ⋅    ⋅    ⋅

julia> spzeros(Float32, 4)
4-element SparseVector{Float32, Int64} with 0 stored entries

spzeros([type], I::AbstractVector, J::AbstractVector, [m, n])

Create a sparse matrix S of dimensions m x n with structural zeros at S[I[k], J[k]].

This method can be used to construct the sparsity pattern of the matrix, and is more efficient than using e.g. sparse(I, J, zeros(length(I))).

For additional documentation and an expert driver, see SparseArrays.spzeros!.

Julia 1.10

This methods requires Julia version 1.10 or later.

SparseArrays.spzeros! — Function

spzeros!(::Type{Tv}, I::AbstractVector{Ti}, J::AbstractVector{Ti}, m::Integer, n::Integer,
         klasttouch::Vector{Ti}, csrrowptr::Vector{Ti}, csrcolval::Vector{Ti},
         [csccolptr::Vector{Ti}], [cscrowval::Vector{Ti}, cscnzval::Vector{Tv}]) where {Tv,Ti<:Integer}

Parent of and expert driver for spzeros(I, J) allowing user to provide preallocated storage for intermediate objects. This method is to spzeros what SparseArrays.sparse! is to sparse. See documentation for SparseArrays.sparse! for details and required buffer lengths.

Julia 1.10

This methods requires Julia version 1.10 or later.

SparseArrays.spzeros!(::Type{Tv}, I, J, [m, n]) -> SparseMatrixCSC{Tv}

Variant of spzeros! that re-uses the input vectors I and J for the final matrix storage. After construction the input vectors will alias the matrix buffers; S.colptr === I and S.rowval === J holds, and they will be resize!d as necessary.

Note that some work buffers will still be allocated. Specifically, this method is a convenience wrapper around spzeros!(Tv, I, J, m, n, klasttouch, csrrowptr, csrcolval, csccolptr, cscrowval) where this method allocates klasttouch, csrrowptr, and csrcolval of appropriate size, but reuses I and J for csccolptr and cscrowval.

Arguments m and n defaults to maximum(I) and maximum(J).

Julia 1.10

This method requires Julia version 1.10 or later.

SparseArrays.spdiagm — Function

spdiagm(kv::Pair{<:Integer,<:AbstractVector}...)
spdiagm(m::Integer, n::Integer, kv::Pair{<:Integer,<:AbstractVector}...)

Construct a sparse diagonal matrix from Pairs of vectors and diagonals. Each vector kv.second will be placed on the kv.first diagonal. By default, the matrix is square and its size is inferred from kv, but a non-square size m×n (padded with zeros as needed) can be specified by passing m,n as the first arguments.

Examples

julia> spdiagm(-1 => [1,2,3,4], 1 => [4,3,2,1])
5×5 SparseMatrixCSC{Int64, Int64} with 8 stored entries:
 ⋅  4  ⋅  ⋅  ⋅
 1  ⋅  3  ⋅  ⋅
 ⋅  2  ⋅  2  ⋅
 ⋅  ⋅  3  ⋅  1
 ⋅  ⋅  ⋅  4  ⋅

spdiagm(v::AbstractVector)
spdiagm(m::Integer, n::Integer, v::AbstractVector)

Construct a sparse matrix with elements of the vector as diagonal elements. By default (no given m and n), the matrix is square and its size is given by length(v), but a non-square size m×n can be specified by passing m and n as the first arguments.

Julia 1.6

These functions require at least Julia 1.6.

Examples

julia> spdiagm([1,2,3])
3×3 SparseMatrixCSC{Int64, Int64} with 3 stored entries:
 1  ⋅  ⋅
 ⋅  2  ⋅
 ⋅  ⋅  3

julia> spdiagm(sparse([1,0,3]))
3×3 SparseMatrixCSC{Int64, Int64} with 2 stored entries:
 1  ⋅  ⋅
 ⋅  ⋅  ⋅
 ⋅  ⋅  3

SparseArrays.sparse_hcat — Function

sparse_hcat(A...)

Concatenate along dimension 2. Return a SparseMatrixCSC object.

Julia 1.8

This method was added in Julia 1.8. It mimics previous concatenation behavior, where the concatenation with specialized "sparse" matrix types from LinearAlgebra.jl automatically yielded sparse output even in the absence of any SparseArray argument.

SparseArrays.sparse_vcat — Function

sparse_vcat(A...)

Concatenate along dimension 1. Return a SparseMatrixCSC object.

Julia 1.8

SparseArrays.sparse_hvcat — Function

sparse_hvcat(rows::Tuple{Vararg{Int}}, values...)

Sparse horizontal and vertical concatenation in one call. This function is called for block matrix syntax. The first argument specifies the number of arguments to concatenate in each block row.

Julia 1.8

SparseArrays.blockdiag — Function

blockdiag(A...)

Concatenate matrices block-diagonally. Currently only implemented for sparse matrices.

Examples

julia> blockdiag(sparse(2I, 3, 3), sparse(4I, 2, 2))
5×5 SparseMatrixCSC{Int64, Int64} with 5 stored entries:
 2  ⋅  ⋅  ⋅  ⋅
 ⋅  2  ⋅  ⋅  ⋅
 ⋅  ⋅  2  ⋅  ⋅
 ⋅  ⋅  ⋅  4  ⋅
 ⋅  ⋅  ⋅  ⋅  4

SparseArrays.sprand — Function

sprand([rng],[T::Type],m,[n],p::AbstractFloat)
sprand([rng],m,[n],p::AbstractFloat,[rfn=rand])

Create a random length m sparse vector or m by n sparse matrix, in which the probability of any element being nonzero is independently given by p (and hence the mean density of nonzeros is also exactly p). The optional rng argument specifies a random number generator, see Random Numbers. The optional T argument specifies the element type, which defaults to Float64.

By default, nonzero values are sampled from a uniform distribution using the rand function, i.e. by rand(T), or rand(rng, T) if rng is supplied; for the default T=Float64, this corresponds to nonzero values sampled uniformly in [0,1).

You can sample nonzero values from a different distribution by passing a custom rfn function instead of rand. This should be a function rfn(k) that returns an array of k random numbers sampled from the desired distribution; alternatively, if rng is supplied, it should instead be a function rfn(rng, k).

Examples

julia> sprand(Bool, 2, 2, 0.5)
2×2 SparseMatrixCSC{Bool, Int64} with 2 stored entries:
 1  1
 ⋅  ⋅

julia> sprand(Float64, 3, 0.75)
3-element SparseVector{Float64, Int64} with 2 stored entries:
  [1]  =  0.795547
  [2]  =  0.49425

SparseArrays.sprandn — Function

sprandn([rng][,Type],m[,n],p::AbstractFloat)

Create a random sparse vector of length m or sparse matrix of size m by n with the specified (independent) probability p of any entry being nonzero, where nonzero values are sampled from the normal distribution. The optional rng argument specifies a random number generator, see Random Numbers.

Julia 1.1

Specifying the output element type Type requires at least Julia 1.1.

Examples

julia> sprandn(2, 2, 0.75)
2×2 SparseMatrixCSC{Float64, Int64} with 3 stored entries:
 -1.20577     ⋅
  0.311817  -0.234641

SparseArrays.nonzeros — Function

nonzeros(A)

Return a vector of the structural nonzero values in sparse array A. This includes zeros that are explicitly stored in the sparse array. The returned vector points directly to the internal nonzero storage of A, and any modifications to the returned vector will mutate A as well. See rowvals and nzrange.

Examples

julia> A = sparse(2I, 3, 3)
3×3 SparseMatrixCSC{Int64, Int64} with 3 stored entries:
 2  ⋅  ⋅
 ⋅  2  ⋅
 ⋅  ⋅  2

julia> nonzeros(A)
3-element Vector{Int64}:
 2
 2
 2

SparseArrays.rowvals — Function

rowvals(A::AbstractSparseMatrixCSC)

Return a vector of the row indices of A. Any modifications to the returned vector will mutate A as well. Providing access to how the row indices are stored internally can be useful in conjunction with iterating over structural nonzero values. See also nonzeros and nzrange.

Examples

julia> A = sparse(2I, 3, 3)
3×3 SparseMatrixCSC{Int64, Int64} with 3 stored entries:
 2  ⋅  ⋅
 ⋅  2  ⋅
 ⋅  ⋅  2

julia> rowvals(A)
3-element Vector{Int64}:
 1
 2
 3

SparseArrays.nzrange — Function

nzrange(A::AbstractSparseMatrixCSC, col::Integer)

Return the range of indices to the structural nonzero values of a sparse matrix column. In conjunction with nonzeros and rowvals, this allows for convenient iterating over a sparse matrix :

A = sparse(I,J,V)
rows = rowvals(A)
vals = nonzeros(A)
m, n = size(A)
for j = 1:n
   for i in nzrange(A, j)
      row = rows[i]
      val = vals[i]
      # perform sparse wizardry...
   end
end

Warning

Adding or removing nonzero elements to the matrix may invalidate the nzrange, one should not mutate the matrix while iterating.

nzrange(x::SparseVectorUnion, col)

Give the range of indices to the structural nonzero values of a sparse vector. The column index col is ignored (assumed to be 1).

SparseArrays.droptol! — Function

droptol!(A::AbstractSparseMatrixCSC, tol)

Removes stored values from A whose absolute value is less than or equal to tol.

droptol!(x::AbstractCompressedVector, tol)

Removes stored values from x whose absolute value is less than or equal to tol.

SparseArrays.dropzeros! — Function

dropzeros!(x::AbstractCompressedVector)

Removes stored numerical zeros from x.

For an out-of-place version, see dropzeros. For algorithmic information, see fkeep!.

SparseArrays.dropzeros — Function

dropzeros(A::AbstractSparseMatrixCSC;)

Generates a copy of A and removes stored numerical zeros from that copy.

For an in-place version and algorithmic information, see dropzeros!.

Examples

julia> A = sparse([1, 2, 3], [1, 2, 3], [1.0, 0.0, 1.0])
3×3 SparseMatrixCSC{Float64, Int64} with 3 stored entries:
 1.0   ⋅    ⋅
  ⋅   0.0   ⋅
  ⋅    ⋅   1.0

julia> dropzeros(A)
3×3 SparseMatrixCSC{Float64, Int64} with 2 stored entries:
 1.0   ⋅    ⋅
  ⋅    ⋅    ⋅
  ⋅    ⋅   1.0

dropzeros(x::AbstractCompressedVector)

Generates a copy of x and removes numerical zeros from that copy.

For an in-place version and algorithmic information, see dropzeros!.

Examples

julia> A = sparsevec([1, 2, 3], [1.0, 0.0, 1.0])
3-element SparseVector{Float64, Int64} with 3 stored entries:
  [1]  =  1.0
  [2]  =  0.0
  [3]  =  1.0

julia> dropzeros(A)
3-element SparseVector{Float64, Int64} with 2 stored entries:
  [1]  =  1.0
  [3]  =  1.0

SparseArrays.permute — Function

permute(A::AbstractSparseMatrixCSC{Tv,Ti}, p::AbstractVector{<:Integer},
        q::AbstractVector{<:Integer}) where {Tv,Ti}

Bilaterally permute A, returning PAQ (A[p,q]). Column-permutation q's length must match A's column count (length(q) == size(A, 2)). Row-permutation p's length must match A's row count (length(p) == size(A, 1)).

For expert drivers and additional information, see permute!.

Examples

julia> A = spdiagm(0 => [1, 2, 3, 4], 1 => [5, 6, 7])
4×4 SparseMatrixCSC{Int64, Int64} with 7 stored entries:
 1  5  ⋅  ⋅
 ⋅  2  6  ⋅
 ⋅  ⋅  3  7
 ⋅  ⋅  ⋅  4

julia> permute(A, [4, 3, 2, 1], [1, 2, 3, 4])
4×4 SparseMatrixCSC{Int64, Int64} with 7 stored entries:
 ⋅  ⋅  ⋅  4
 ⋅  ⋅  3  7
 ⋅  2  6  ⋅
 1  5  ⋅  ⋅

julia> permute(A, [1, 2, 3, 4], [4, 3, 2, 1])
4×4 SparseMatrixCSC{Int64, Int64} with 7 stored entries:
 ⋅  ⋅  5  1
 ⋅  6  2  ⋅
 7  3  ⋅  ⋅
 4  ⋅  ⋅  ⋅

Base.permute! — Method

permute!(X::AbstractSparseMatrixCSC{Tv,Ti}, A::AbstractSparseMatrixCSC{Tv,Ti},
         p::AbstractVector{<:Integer}, q::AbstractVector{<:Integer},
         [C::AbstractSparseMatrixCSC{Tv,Ti}]) where {Tv,Ti}

Bilaterally permute A, storing result PAQ (A[p,q]) in X. Stores intermediate result (AQ)^T (transpose(A[:,q])) in optional argument C if present. Requires that none of X, A, and, if present, C alias each other; to store result PAQ back into A, use the following method lacking X:

permute!(A::AbstractSparseMatrixCSC{Tv,Ti}, p::AbstractVector{<:Integer},
         q::AbstractVector{<:Integer}[, C::AbstractSparseMatrixCSC{Tv,Ti},
         [workcolptr::Vector{Ti}]]) where {Tv,Ti}

X's dimensions must match those of A (size(X, 1) == size(A, 1) and size(X, 2) == size(A, 2)), and X must have enough storage to accommodate all allocated entries in A (length(rowvals(X)) >= nnz(A) and length(nonzeros(X)) >= nnz(A)). Column-permutation q's length must match A's column count (length(q) == size(A, 2)). Row-permutation p's length must match A's row count (length(p) == size(A, 1)).

C's dimensions must match those of transpose(A) (size(C, 1) == size(A, 2) and size(C, 2) == size(A, 1)), and C must have enough storage to accommodate all allocated entries in A (length(rowvals(C)) >= nnz(A) and length(nonzeros(C)) >= nnz(A)).

For additional (algorithmic) information, and for versions of these methods that forgo argument checking, see (unexported) parent methods unchecked_noalias_permute! and unchecked_aliasing_permute!.

Noteworthy External Sparse Packages

Several other Julia packages provide sparse matrix implementations that should be mentioned:

SuiteSparseGraphBLAS.jl is a wrapper over the fast, multithreaded SuiteSparse:GraphBLAS C library. On CPU this is typically the fastest option, often significantly outperforming MKLSparse.
CUDA.jl exposes the CUSPARSE library for GPU sparse matrix operations.
SparseMatricesCSR.jl provides a Julia native implementation of the Compressed Sparse Rows (CSR) format.
MKLSparse.jl accelerates SparseArrays sparse-dense matrix operations using Intel's MKL library.
SparseArrayKit.jl available for multidimensional sparse arrays.
LuxurySparse.jl provides static sparse array formats, as well as a coordinate format.
ExtendableSparse.jl enables fast insertion into sparse matrices using a lazy approach to new stored indices.
Finch.jl supports extensive multidimensional sparse array formats and operations through a mini tensor language and compiler, all in native Julia. Support for COO, CSF, CSR, CSC and more, as well as operations like broadcast, reduce, etc. and custom operations.