# Sparse Matrix Operations

For sparse matrices, for which many elements are zero, it is more efficient to use specialized representations to save memory and speed up matrix arithmetic (including derivative calculations). Given Stan’s implementation, there is substantial space (memory) savings by using sparse matrices. Because of the ease of optimizing dense matrix operations, speed improvements only arise at 90% or even greater sparsity; below that level, dense matrices are faster but use more memory.

Because of this speedup and space savings, it may even be useful to read in a dense matrix and convert it to a sparse matrix before multiplying it by a vector. This chapter covers a very specific form of sparsity consisting of a sparse matrix multiplied by a dense vector.

## Compressed row storage

Sparse matrices are represented in Stan using compressed row storage (CSR). For example, the matrix $\begin{equation*} A = \begin{bmatrix} 19 & 27 & 0 & 0 \\ 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 52 \\ 81 & 0 & 95 & 33 \end{bmatrix} \end{equation*}$ is translated into a vector of the non-zero real values, read by row from the matrix $$A$$, $\begin{equation*} w(A) = \begin{bmatrix} 19 & 27 & 52 & 81 & 95 & 33 \end{bmatrix}^{\top} \! \! \! , \end{equation*}$ an array of integer column indices for the values, $\begin{equation*} v(A) = \begin{bmatrix} 1 & 2 & 4 & 1 & 3 & 4 \end{bmatrix} \! , \end{equation*}$ and an array of integer indices indicating where in $$w(A)$$ a given row’s values start, $\begin{equation*} u(A) = \begin{bmatrix} 1 & 3 & 3 & 4 & 7 \end{bmatrix} \! , \end{equation*}$ with a padded value at the end to guarantee that $\begin{equation*} u(A)[n+1] - u(A)[n] \end{equation*}$ is the number of non-zero elements in row $$n$$ of the matrix (here $$2$$, $$0$$, $$1$$, and $$3$$). Note that because the second row has no non-zero elements both the second and third elements of $$u(A)$$ correspond to the third element of $$w(A)$$, which is $$52$$. The values $$(w(A), \, v(A), \, u(A))$$ are sufficient to reconstruct $$A$$.

The values are structured so that there is a real value and integer column index for each non-zero entry in the array, plus one integer for each row of the matrix, plus one for padding. There is also underlying storage for internal container pointers and sizes. The total memory usage is roughly $$12 K + M$$ bytes plus a small constant overhead, which is often considerably fewer bytes than the $$M \times N$$ required to store a dense matrix. Even more importantly, zero values do not introduce derivatives under multiplication or addition, so many storage and evaluation steps are saved when sparse matrices are multiplied.

## Conversion functions

Conversion functions between dense and sparse matrices are provided.

### Dense to sparse conversion

Converting a dense matrix $$m$$ to a sparse representation produces a vector $$w$$ and two integer arrays, $$u$$ and $$v$$.

vector csr_extract_w(matrix a)
Return non-zero values in matrix a; see section compressed row storage.

Available since 2.8

array[] int csr_extract_v(matrix a)
Return column indices for values in csr_extract_w(a); see compressed row storage.

Available since 2.8

array[] int csr_extract_u(matrix a)
Return array of row starting indices for entries in csr_extract_w(a) followed by the size of csr_extract_w(a) plus one; see section compressed row storage.

Available since 2.8

tuple(vector, array[] int, array[] int) csr_extract(matrix a)
Return all three components of the CSR representation of the matrix a; see section compressed row storage. This function is equivalent to (csr_extract_w(a), csr_extract_v(a), csr_extract_u(a)).

Available since 2.33

### Sparse to dense conversion

To convert a sparse matrix representation to a dense matrix, there is a single function.

matrix csr_to_dense_matrix(int m, int n, vector w, array[] int v, array[] int u)
Return dense $$\text{m} \times \text{n}$$ matrix with non-zero matrix entries w, column indices v, and row starting indices u; the vector w and array v must be the same size (corresponding to the total number of nonzero entries in the matrix), array v must have index values bounded by m, array u must have length equal to m + 1 and contain index values bounded by the number of nonzeros (except for the last entry, which must be equal to the number of nonzeros plus one). See section compressed row storage for more details.

Available since 2.10

## Sparse matrix arithmetic

### Sparse matrix multiplication

The only supported operation is the multiplication of a sparse matrix $$A$$ and a dense vector $$b$$ to produce a dense vector $$A\,b$$. Multiplying a dense row vector $$b$$ and a sparse matrix $$A$$ can be coded using transposition as $\begin{equation*} b \, A = (A^{\top} \, b^{\top})^{\top}, \end{equation*}$ but care must be taken to represent $$A^{\top}$$ rather than $$A$$ as a sparse matrix.

vector csr_matrix_times_vector(int m, int n, vector w, array[] int v, array[] int u, vector b)
Multiply the $$\text{m} \times \text{n}$$ matrix represented by values w, column indices v, and row start indices u by the vector b; see compressed row storage.

Available since 2.18