# Arrays¶

## Basic functions¶

ndims(A) → Integer

Returns the number of dimensions of A

size(A[, dim...])

Returns a tuple containing the dimensions of A. Optionally you can specify the dimension(s) you want the length of, and get the length of that dimension, or a tuple of the lengths of dimensions you asked for.:

julia> A = rand(2,3,4);

julia> size(A, 2)
3

julia> size(A,3,2)
(4,3)

iseltype(A, T)

Tests whether A or its elements are of type T.

length(A) → Integer

Returns the number of elements in A.

eachindex(A...)

Creates an iterable object for visiting each index of an AbstractArray A in an efficient manner. For array types that have opted into fast linear indexing (like Array), this is simply the range 1:length(A). For other array types, this returns a specialized Cartesian range to efficiently index into the array with indices specified for every dimension. For other iterables, including strings and dictionaries, this returns an iterator object supporting arbitrary index types (e.g. unevenly spaced or non-integer indices).

Example for a sparse 2-d array:

julia> A = sparse([1, 1, 2], [1, 3, 1], [1, 2, -5])
2x3 sparse matrix with 3 Int64 entries:
[1, 1]  =  1
[2, 1]  =  -5
[1, 3]  =  2

julia> for iter in eachindex(A)
@show iter.I[1], iter.I[2]
@show A[iter]
end
(iter.I[1],iter.I[2]) = (1,1)
A[iter] = 1
(iter.I[1],iter.I[2]) = (2,1)
A[iter] = -5
(iter.I[1],iter.I[2]) = (1,2)
A[iter] = 0
(iter.I[1],iter.I[2]) = (2,2)
A[iter] = 0
(iter.I[1],iter.I[2]) = (1,3)
A[iter] = 2
(iter.I[1],iter.I[2]) = (2,3)
A[iter] = 0


If you supply more than one AbstractArray argument, eachindex will create an iterable object that is fast for all arguments (a UnitRange if all inputs have fast linear indexing, a CartesianRange otherwise). If the arrays have different sizes and/or dimensionalities, eachindex returns an iterable that spans the largest range along each dimension.

Base.linearindexing(A)

linearindexing defines how an AbstractArray most efficiently accesses its elements. If Base.linearindexing(A) returns Base.LinearFast(), this means that linear indexing with only one index is an efficient operation. If it instead returns Base.LinearSlow() (by default), this means that the array intrinsically accesses its elements with indices specified for every dimension. Since converting a linear index to multiple indexing subscripts is typically very expensive, this provides a traits-based mechanism to enable efficient generic code for all array types.

An abstract array subtype MyArray that wishes to opt into fast linear indexing behaviors should define linearindexing in the type-domain:

Base.linearindexing{T<:MyArray}(::Type{T}) = Base.LinearFast()

countnz(A)

Counts the number of nonzero values in array A (dense or sparse). Note that this is not a constant-time operation. For sparse matrices, one should usually use nnz, which returns the number of stored values.

conj!(A)

Convert an array to its complex conjugate in-place

stride(A, k)

Returns the distance in memory (in number of elements) between adjacent elements in dimension k.

strides(A)

Returns a tuple of the memory strides in each dimension

ind2sub(dims, index) → subscripts

Returns a tuple of subscripts into an array with dimensions dims, corresponding to the linear index index.

Example: i, j, ... = ind2sub(size(A), indmax(A)) provides the indices of the maximum element

ind2sub(a, index) → subscripts

Returns a tuple of subscripts into array a corresponding to the linear index index

sub2ind(dims, i, j, k...) → index

The inverse of ind2sub, returns the linear index corresponding to the provided subscripts

## Constructors¶

Array(dims)

Array{T}(dims) constructs an uninitialized dense array with element type T. dims may be a tuple or a series of integer arguments. The syntax Array(T, dims) is also available, but deprecated.

getindex(type[, elements...])

Construct a 1-d array of the specified type. This is usually called with the syntax Type[]. Element values can be specified using Type[a,b,c,...].

cell(dims)

Construct an uninitialized cell array (heterogeneous array). dims can be either a tuple or a series of integer arguments.

zeros(type, dims)

Create an array of all zeros of specified type. The type defaults to Float64 if not specified.

zeros(A)

Create an array of all zeros with the same element type and shape as A.

ones(type, dims)

Create an array of all ones of specified type. The type defaults to Float64 if not specified.

ones(A)

Create an array of all ones with the same element type and shape as A.

trues(dims)

Create a BitArray with all values set to true.

trues(A)

Create a BitArray with all values set to true of the same shape as A.

falses(dims)

Create a BitArray with all values set to false.

falses(A)

Create a BitArray with all values set to false of the same shape as A.

fill(x, dims)

Create an array filled with the value x. For example, fill(1.0, (10,10)) returns a 10x10 array of floats, with each element initialized to 1.0.

If x is an object reference, all elements will refer to the same object. fill(Foo(), dims) will return an array filled with the result of evaluating Foo() once.

fill!(A, x)

Fill array A with the value x. If x is an object reference, all elements will refer to the same object. fill!(A, Foo()) will return A filled with the result of evaluating Foo() once.

reshape(A, dims)

Create an array with the same data as the given array, but with different dimensions. An implementation for a particular type of array may choose whether the data is copied or shared.

similar(array[, element_type=eltype(array)][, dims=size(array)])

Create an uninitialized mutable array with the given element type and size, based upon the given source array. The second and third arguments are both optional, defaulting to the given array’s eltype and size. The dimensions may be specified either as a single tuple argument or as a series of integer arguments.

Custom AbstractArray subtypes may choose which specific array type is best-suited to return for the given element type and dimensionality. If they do not specialize this method, the default is an Array(element_type, dims...).

For example, similar(1:10, 1, 4) returns an uninitialized Array{Int,2} since ranges are neither mutable nor support 2 dimensions:

julia> similar(1:10, 1, 4)
1x4 Array{Int64,2}:
4419743872  4374413872  4419743888  0


Conversely, similar(trues(10,10), 2) returns an uninitialized BitVector with two elements since BitArrays are both mutable and can support 1-dimensional arrays:

julia> similar(trues(10,10), 2)
2-element BitArray{1}:
false
false


Since BitArrays can only store elements of type Bool, however, if you request a different element type it will create a regular Array instead:

julia> similar(falses(10), Float64, 2, 4)
2x4 Array{Float64,2}:
2.18425e-314  2.18425e-314  2.18425e-314  2.18425e-314
2.18425e-314  2.18425e-314  2.18425e-314  2.18425e-314

reinterpret(type, A)

Change the type-interpretation of a block of memory. For example, reinterpret(Float32, UInt32(7)) interprets the 4 bytes corresponding to UInt32(7) as a Float32. For arrays, this constructs an array with the same binary data as the given array, but with the specified element type.

eye(n)

n-by-n identity matrix

eye(m, n)

m-by-n identity matrix

eye(A)

Constructs an identity matrix of the same dimensions and type as A.

linspace(start, stop, n=100)

Construct a range of n linearly spaced elements from start to stop.

logspace(start, stop, n=50)

Construct a vector of n logarithmically spaced numbers from 10^start to 10^stop.

## Mathematical operators and functions¶

All mathematical operations and functions are supported for arrays

broadcast(f, As...)

Broadcasts the arrays As to a common size by expanding singleton dimensions, and returns an array of the results f(as...) for each position.

broadcast!(f, dest, As...)

Like broadcast, but store the result of broadcast(f, As...) in the dest array. Note that dest is only used to store the result, and does not supply arguments to f unless it is also listed in the As, as in broadcast!(f, A, A, B) to perform A[:] = broadcast(f, A, B).

bitbroadcast(f, As...)

Like broadcast, but allocates a BitArray to store the result, rather then an Array.

broadcast_function(f)

Returns a function broadcast_f such that broadcast_function(f)(As...) === broadcast(f, As...). Most useful in the form const broadcast_f = broadcast_function(f).

broadcast!_function(f)

Like broadcast_function, but for broadcast!.

## Indexing, Assignment, and Concatenation¶

getindex(A, inds...)

Returns a subset of array A as specified by inds, where each ind may be an Int, a Range, or a Vector. See the manual section on array indexing for details.

sub(A, inds...)

Like getindex(), but returns a view into the parent array A with the given indices instead of making a copy. Calling getindex() or setindex!() on the returned SubArray computes the indices to the parent array on the fly without checking bounds.

parent(A)

Returns the “parent array” of an array view type (e.g., SubArray), or the array itself if it is not a view

parentindexes(A)

From an array view A, returns the corresponding indexes in the parent

slicedim(A, d, i)

Return all the data of A where the index for dimension d equals i. Equivalent to A[:,:,...,i,:,:,...] where i is in position d.

slice(A, inds...)

Returns a view of array A with the given indices like sub(), but drops all dimensions indexed with scalars.

setindex!(A, X, inds...)

Store values from array X within some subset of A as specified by inds.

broadcast_getindex(A, inds...)

Broadcasts the inds arrays to a common size like broadcast, and returns an array of the results A[ks...], where ks goes over the positions in the broadcast.

broadcast_setindex!(A, X, inds...)

Broadcasts the X and inds arrays to a common size and stores the value from each position in X at the indices given by the same positions in inds.

cat(dims, A...)

Concatenate the input arrays along the specified dimensions in the iterable dims. For dimensions not in dims, all input arrays should have the same size, which will also be the size of the output array along that dimension. For dimensions in dims, the size of the output array is the sum of the sizes of the input arrays along that dimension. If dims is a single number, the different arrays are tightly stacked along that dimension. If dims is an iterable containing several dimensions, this allows one to construct block diagonal matrices and their higher-dimensional analogues by simultaneously increasing several dimensions for every new input array and putting zero blocks elsewhere. For example, cat([1,2], matrices...) builds a block diagonal matrix, i.e. a block matrix with matrices[1], matrices[2], ... as diagonal blocks and matching zero blocks away from the diagonal.

vcat(A...)

Concatenate along dimension 1

hcat(A...)

Concatenate along dimension 2

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

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> a, b, c, d, e, f = 1, 2, 3, 4, 5, 6
(1,2,3,4,5,6)

julia> [a b c; d e f]
2x3 Array{Int64,2}:
1  2  3
4  5  6

julia> hvcat((3,3), a,b,c,d,e,f)
2x3 Array{Int64,2}:
1  2  3
4  5  6

julia> [a b;c d; e f]
3x2 Array{Int64,2}:
1  2
3  4
5  6

julia> hvcat((2,2,2), a,b,c,d,e,f)
3x2 Array{Int64,2}:
1  2
3  4
5  6


If the first argument is a single integer n, then all block rows are assumed to have n block columns.

flipdim(A, d)

Reverse A in dimension d.

circshift(A, shifts)

Circularly shift the data in an array. The second argument is a vector giving the amount to shift in each dimension.

find(A)

Return a vector of the linear indexes of the non-zeros in A (determined by A[i]!=0). A common use of this is to convert a boolean array to an array of indexes of the true elements.

find(f, A)

Return a vector of the linear indexes of A where f returns true.

findn(A)

Return a vector of indexes for each dimension giving the locations of the non-zeros in A (determined by A[i]!=0).

findnz(A)

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

findfirst(A)

Return the index of the first non-zero value in A (determined by A[i]!=0).

findfirst(A, v)

Return the index of the first element equal to v in A.

findfirst(predicate, A)

Return the index of the first element of A for which predicate returns true.

findlast(A)

Return the index of the last non-zero value in A (determined by A[i]!=0).

findlast(A, v)

Return the index of the last element equal to v in A.

findlast(predicate, A)

Return the index of the last element of A for which predicate returns true.

findnext(A, i)

Find the next index >= i of a non-zero element of A, or 0 if not found.

findnext(predicate, A, i)

Find the next index >= i of an element of A for which predicate returns true, or 0 if not found.

findnext(A, v, i)

Find the next index >= i of an element of A equal to v (using ==), or 0 if not found.

findprev(A, i)

Find the previous index <= i of a non-zero element of A, or 0 if not found.

findprev(predicate, A, i)

Find the previous index <= i of an element of A for which predicate returns true, or 0 if not found.

findprev(A, v, i)

Find the previous index <= i of an element of A equal to v (using ==), or 0 if not found.

permutedims(A, perm)

Permute the dimensions of array A. perm is a vector specifying a permutation of length ndims(A). This is a generalization of transpose for multi-dimensional arrays. Transpose is equivalent to permutedims(A, [2,1]).

ipermutedims(A, perm)

Like permutedims(), except the inverse of the given permutation is applied.

permutedims!(dest, src, perm)

Permute the dimensions of array src and store the result in the array dest. perm is a vector specifying a permutation of length ndims(src). The preallocated array dest should have size(dest) == size(src)[perm] and is completely overwritten. No in-place permutation is supported and unexpected results will happen if src and dest have overlapping memory regions.

squeeze(A, dims)

Remove the dimensions specified by dims from array A. Elements of dims must be unique and within the range 1:ndims(A).

vec(Array) → Vector

Vectorize an array using column-major convention.

promote_shape(s1, s2)

Check two array shapes for compatibility, allowing trailing singleton dimensions, and return whichever shape has more dimensions.

checkbounds(array, indexes...)

Throw an error if the specified indexes are not in bounds for the given array. Subtypes of AbstractArray should specialize this method if they need to provide custom bounds checking behaviors.

checkbounds(::Type{Bool}, dimlength::Integer, index)

Return a Bool describing if the given index is within the bounds of the given dimension length. Custom types that would like to behave as indices for all arrays can extend this method in order to provide a specialized bounds checking implementation.

randsubseq(A, p) → Vector

Return a vector consisting of a random subsequence of the given array A, where each element of A is included (in order) with independent probability p. (Complexity is linear in p*length(A), so this function is efficient even if p is small and A is large.) Technically, this process is known as “Bernoulli sampling” of A.

randsubseq!(S, A, p)

Like randsubseq, but the results are stored in S (which is resized as needed).

## Array functions¶

cumprod(A[, dim])

Cumulative product along a dimension dim (defaults to 1). See also cumprod!() to use a preallocated output array, both for performance and to control the precision of the output (e.g. to avoid overflow).

cumprod!(B, A[, dim])

Cumulative product of A along a dimension, storing the result in B. The dimension defaults to 1.

cumsum(A[, dim])

Cumulative sum along a dimension dim (defaults to 1). See also cumsum!() to use a preallocated output array, both for performance and to control the precision of the output (e.g. to avoid overflow).

cumsum!(B, A[, dim])

Cumulative sum of A along a dimension, storing the result in B. The dimension defaults to 1.

cumsum_kbn(A[, dim])

Cumulative sum along a dimension, using the Kahan-Babuska-Neumaier compensated summation algorithm for additional accuracy. The dimension defaults to 1.

cummin(A[, dim])

Cumulative minimum along a dimension. The dimension defaults to 1.

cummax(A[, dim])

Cumulative maximum along a dimension. The dimension defaults to 1.

diff(A[, dim])

Finite difference operator of matrix or vector.

gradient(F[, h])

Compute differences along vector F, using h as the spacing between points. The default spacing is one.

rot180(A)

Rotate matrix A 180 degrees.

rot180(A, k)

Rotate matrix A 180 degrees an integer k number of times. If k is even, this is equivalent to a copy.

rotl90(A)

Rotate matrix A left 90 degrees.

rotl90(A, k)

Rotate matrix A left 90 degrees an integer k number of times. If k is zero or a multiple of four, this is equivalent to a copy.

rotr90(A)

Rotate matrix A right 90 degrees.

rotr90(A, k)

Rotate matrix A right 90 degrees an integer k number of times. If k is zero or a multiple of four, this is equivalent to a copy.

reducedim(f, A, dims[, initial])

Reduce 2-argument function f along dimensions of A. dims is a vector specifying the dimensions to reduce, and initial is the initial value to use in the reductions. For +, *, max and min the initial argument is optional.

The associativity of the reduction is implementation-dependent; if you need a particular associativity, e.g. left-to-right, you should write your own loop. See documentation for reduce.

mapreducedim(f, op, A, dims[, initial])

Evaluates to the same as reducedim(op, map(f, A), dims, f(initial)), but is generally faster because the intermediate array is avoided.

mapslices(f, A, dims)

Transform the given dimensions of array A using function f. f is called on each slice of A of the form A[...,:,...,:,...]. dims is an integer vector specifying where the colons go in this expression. The results are concatenated along the remaining dimensions. For example, if dims is [1,2] and A is 4-dimensional, f is called on A[:,:,i,j] for all i and j.

sum_kbn(A)

Returns the sum of all array elements, using the Kahan-Babuska-Neumaier compensated summation algorithm for additional accuracy.

## Combinatorics¶

nthperm(v, k)

Compute the kth lexicographic permutation of a vector.

nthperm(p)

Return the k that generated permutation p. Note that nthperm(nthperm([1:n], k)) == k for 1 <= k <= factorial(n).

nthperm!(v, k)

In-place version of nthperm().

randperm([rng, ]n)

Construct a random permutation of length n. The optional rng argument specifies a random number generator, see Random Numbers.

invperm(v)

Return the inverse permutation of v.

isperm(v) → Bool

Returns true if v is a valid permutation.

permute!(v, p)

Permute vector v in-place, according to permutation p. No checking is done to verify that p is a permutation.

To return a new permutation, use v[p]. Note that this is generally faster than permute!(v,p) for large vectors.

ipermute!(v, p)

Like permute!, but the inverse of the given permutation is applied.

randcycle([rng, ]n)

Construct a random cyclic permutation of length n. The optional rng argument specifies a random number generator, see Random Numbers.

shuffle([rng, ]v)

Return a randomly permuted copy of v. The optional rng argument specifies a random number generator, see Random Numbers.

shuffle!([rng, ]v)

In-place version of shuffle().

reverse(v[, start=1[, stop=length(v)]])

Return a copy of v reversed from start to stop.

reverseind(v, i)

Given an index i in reverse(v), return the corresponding index in v so that v[reverseind(v,i)] == reverse(v)[i]. (This can be nontrivial in the case where v is a Unicode string.)

reverse!(v[, start=1[, stop=length(v)]]) → v

In-place version of reverse().

combinations(array, n)

Generate all combinations of n elements from an indexable object. Because the number of combinations can be very large, this function returns an iterator object. Use collect(combinations(array,n)) to get an array of all combinations.

permutations(array)

Generate all permutations of an indexable object. Because the number of permutations can be very large, this function returns an iterator object. Use collect(permutations(array)) to get an array of all permutations.

partitions(n)

Generate all integer arrays that sum to n. Because the number of partitions can be very large, this function returns an iterator object. Use collect(partitions(n)) to get an array of all partitions. The number of partitions to generate can be efficiently computed using length(partitions(n)).

partitions(n, m)

Generate all arrays of m integers that sum to n. Because the number of partitions can be very large, this function returns an iterator object. Use collect(partitions(n,m)) to get an array of all partitions. The number of partitions to generate can be efficiently computed using length(partitions(n,m)).

partitions(array)

Generate all set partitions of the elements of an array, represented as arrays of arrays. Because the number of partitions can be very large, this function returns an iterator object. Use collect(partitions(array)) to get an array of all partitions. The number of partitions to generate can be efficiently computed using length(partitions(array)).

partitions(array, m)

Generate all set partitions of the elements of an array into exactly m subsets, represented as arrays of arrays. Because the number of partitions can be very large, this function returns an iterator object. Use collect(partitions(array,m)) to get an array of all partitions. The number of partitions into m subsets is equal to the Stirling number of the second kind and can be efficiently computed using length(partitions(array,m)).

## BitArrays¶

bitpack(A::AbstractArray{T, N}) → BitArray

Converts a numeric array to a packed boolean array

bitunpack(B::BitArray{N}) → Array{Bool,N}

Converts a packed boolean array to an array of booleans

flipbits!(B::BitArray{N}) → BitArray{N}

Performs a bitwise not operation on B. See ~ operator.

rol!(dest::BitArray{1}, src::BitArray{1}, i::Integer) → BitArray{1}

Performs a left rotation operation on src and put the result into dest.

rol!(B::BitArray{1}, i::Integer) → BitArray{1}

Performs a left rotation operation on B.

rol(B::BitArray{1}, i::Integer) → BitArray{1}

Performs a left rotation operation.

ror!(dest::BitArray{1}, src::BitArray{1}, i::Integer) → BitArray{1}

Performs a right rotation operation on src and put the result into dest.

ror!(B::BitArray{1}, i::Integer) → BitArray{1}

Performs a right rotation operation on B.

ror(B::BitArray{1}, i::Integer) → BitArray{1}

Performs a right rotation operation.

## Sparse Matrices¶

Sparse matrices support much of the same set of operations as dense matrices. The following functions are specific to sparse matrices.

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, duplicates are added by default. All elements of I must satisfy 1 <= I[k] <= m, and all elements of J must satisfy 1 <= J[k] <= n.

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

Create a sparse matrix S of size m x 1 such that S[I[k]] = V[k]. Duplicates are combined using the combine function, which defaults to + if it is not provided. In julia, sparse vectors are really just sparse matrices with one column. Given Julia’s Compressed Sparse Columns (CSC) storage format, a sparse column matrix with one column is sparse, whereas a sparse row matrix with one row ends up being dense.

sparsevec(D::Dict[, m])

Create a sparse matrix of size m x 1 where the row values are keys from the dictionary, and the nonzero values are the values from the dictionary.

issparse(S)

Returns true if S is sparse, and false otherwise.

sparse(A)

Convert an AbstractMatrix A into a sparse matrix.

sparsevec(A)

Convert a dense vector A into a sparse matrix of size m x 1. In julia, sparse vectors are really just sparse matrices with one column.

full(S)

Convert a sparse matrix S into a dense matrix.

nnz(A)

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

spzeros(m, n)

Create a sparse matrix of size m x n. This sparse matrix will not contain any nonzero values. No storage will be allocated for nonzero values during construction.

spones(S)

Create a sparse matrix with the same structure as that of S, but with every nonzero element having the value 1.0.

speye(type, m[, n])

Create a sparse identity matrix of specified type of size m x m. In case n is supplied, create a sparse identity matrix of size m x n.

spdiagm(B, d[, m, n])

Construct a sparse diagonal matrix. B is a tuple of vectors containing the diagonals and d is a tuple containing the positions of the diagonals. In the case the input contains only one diagonal, B can be a vector (instead of a tuple) and d can be the diagonal position (instead of a tuple), defaulting to 0 (diagonal). Optionally, m and n specify the size of the resulting sparse matrix.

sprand([rng, ]m, n, p[, rfn])

Create a random 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). Nonzero values are sampled from the distribution specified by rfn. The uniform distribution is used in case rfn is not specified. The optional rng argument specifies a random number generator, see Random Numbers.

sprandn(m, n, p)

Create a random m by n sparse matrix with the specified (independent) probability p of any entry being nonzero, where nonzero values are sampled from the normal distribution.

sprandbool(m, n, p)

Create a random m by n sparse boolean matrix with the specified (independent) probability p of any entry being true.

etree(A[, post])

Compute the elimination tree of a symmetric sparse matrix A from triu(A) and, optionally, its post-ordering permutation.

symperm(A, p)

Return the symmetric permutation of A, which is A[p,p]. A should be symmetric and sparse, where only the upper triangular part of the matrix is stored. This algorithm ignores the lower triangular part of the matrix. Only the upper triangular part of the result is returned as well.

nonzeros(A)

Return a vector of the structural nonzero values in sparse matrix A. This includes zeros that are explicitly stored in the sparse matrix. 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(A) and nzrange(A, col).

rowvals(A)

Return a vector of the row indices of A, and any modifications to the returned vector will mutate A as well. Given the internal storage format of sparse matrices, providing access to how the row indices are stored internally can be useful in conjunction with iterating over structural nonzero values. See nonzeros(A) and nzrange(A, col).

nzrange(A, col)

Return the range of indices to the structural nonzero values of a sparse matrix column. In conjunction with nonzeros(A) and rowvals(A), 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 i = 1:n
for j in nzrange(A, i)
row = rows[j]
val = vals[j]
# perform sparse wizardry...
end
end