# Module extendr_api::prelude::sparse

Expand description

Sparse data structures and algorithms. Sparse matrix data structures.

Most sparse matrix algorithms accept matrices in sparse column-oriented format. This format represents each column of the matrix by storing the row indices of its non-zero elements, as well as their values.

The indices and the values are each stored in a contiguous slice (or group of slices for arbitrary values). In order to specify where each column starts and ends, a slice of size `ncols + 1` stores the start of each column, with the last element being equal to the total number of non-zeros (or the capacity in uncompressed mode).

## §Example

Consider the 4-by-5 matrix:

``````[[10.0, 0.0, 12.0, -1.0, 13.0]
[ 0.0, 0.0, 25.0, -2.0,  0.0]
[ 1.0, 0.0,  0.0,  0.0,  0.0]
[ 4.0, 0.0,  0.0,  0.0,  5.0]]
``````

The matrix is stored as follows:

``````column pointers:  0 |  3 |  3 |  5 |  7 |  9

row indices:    0 |    2 |    3 |    0 |    1 |    0 |    1 |    0 |    3
values     : 10.0 |  1.0 |  4.0 | 12.0 | 25.0 | -1.0 | -2.0 | 13.0 |  5.0
``````

## Modules§

• Sparse linear algebra module.
Contains low level routines and the implementation of their corresponding high level wrappers.
• Sparse matrix binary and ternary operation implementations.
• solversDeprecated
Sparse solvers.
• Useful sparse matrix primitives.

## Structs§

• Sparse matrix in column-major format, either compressed or uncompressed.
• Sparse matrix view in column-major format, either compressed or uncompressed.
• Sparse matrix view in column-major format, either compressed or uncompressed.
• Sparse matrix in column-major format, either compressed or uncompressed.
• Sparse matrix view in column-major format, either compressed or uncompressed.
• Sparse matrix view in column-major format, either compressed or uncompressed.
• Symbolic structure of sparse matrix in column format, either compressed or uncompressed.
• Symbolic view structure of sparse matrix in column format, either compressed or uncompressed.
• Symbolic structure of sparse matrix in row format, either compressed or uncompressed.
• Symbolic view structure of sparse matrix in row format, either compressed or uncompressed.
• The order values should be read in, when constructing/filling from indices and values.

## Enums§

• Sparse Cholesky error.
• Errors that can occur in sparse algorithms.
• Errors that can occur in sparse algorithms.
• Whether the filled values should replace the current matrix values or be added to them.
• Sparse LU error.

## Traits§

• Trait for unsigned integers that can be indexed with.