In numerical analysis and scientific computing, a sparse matrix or sparse array is a matrix in which most of the elements are zero. By contrast, if most of the elements are nonzero, then the matrix is considered dense. The number of zero-valued elements divided by the total number of elements (e.g., m × n for an m × n matrix) is called the sparsity of the matrix (which is equal to 1 minus the density of the matrix). Using those definitions, a matrix will be sparse when its sparsity is greater than 0.5.
Conceptually, sparsity corresponds to systems with few pairwise interactions. Consider a line of balls connected by springs from one to the next: this is a sparse system as only adjacent balls are coupled. By contrast, if the same line of balls had springs connecting each ball to all other balls, the system would correspond to a dense matrix. The concept of sparsity is useful in combinatorics and application areas such as network theory, which have a low density of significant data or connections.
When storing and manipulating sparse matrices on a computer, it is beneficial and often necessary to use specialized algorithms and data structures that take advantage of the sparse structure of the matrix. Specialized computers have been made for sparse matrices, as they are common in the machine learning field. Operations using standard dense-matrix structures and algorithms are slow and inefficient when applied to large sparse matrices as processing and memory are wasted on the zeroes. Sparse data is by nature more easily compressed and thus requires significantly less storage. Some very large sparse matrices are infeasible to manipulate using standard dense-matrix algorithms.
- 1 Storing a sparse matrix
- 2 Special structure
- 3 Reducing fill-in
- 4 Solving sparse matrix equations
- 5 Software
- 6 History
- 7 See also
- 8 Notes
- 9 References
- 10 Further reading
Storing a sparse matrix
A matrix is typically stored as a two-dimensional array. Each entry in the array represents an element ai,j of the matrix and is accessed by the two indices i and j. Conventionally, i is the row index, numbered from top to bottom, and j is the column index, numbered from left to right. For an m × n matrix, the amount of memory required to store the matrix in this format is proportional to m × n (disregarding the fact that the dimensions of the matrix also need to be stored).
In the case of a sparse matrix, substantial memory requirement reductions can be realized by storing only the non-zero entries. Depending on the number and distribution of the non-zero entries, different data structures can be used and yield huge savings in memory when compared to the basic approach. The trade-off is that accessing the individual elements becomes more complex and additional structures are needed to be able to recover the original matrix unambiguously.
Formats can be divided into two groups:
- Those that support efficient modification, such as DOK (Dictionary of keys), LIL (List of lists), or COO (Coordinate list). These are typically used to construct the matrices.
- Those that support efficient access and matrix operations, such as CSR (Compressed Sparse Row) or CSC (Compressed Sparse Column).
Dictionary of keys (DOK)
DOK consists of a dictionary that maps (row, column)-pairs to the value of the elements. Elements that are missing from the dictionary are taken to be zero. The format is good for incrementally constructing a sparse matrix in random order, but poor for iterating over non-zero values in lexicographical order. One typically constructs a matrix in this format and then converts to another more efficient format for processing.
List of lists (LIL)
LIL stores one list per row, with each entry containing the column index and the value. Typically, these entries are kept sorted by column index for faster lookup. This is another format good for incremental matrix construction.
Coordinate list (COO)
COO stores a list of (row, column, value) tuples. Ideally, the entries are sorted first by row index and then by column index, to improve random access times. This is another format that is good for incremental matrix construction.
Compressed sparse row (CSR, CRS or Yale format)
The compressed sparse row (CSR) or compressed row storage (CRS) or Yale format represents a matrix M by three (one-dimensional) arrays, that respectively contain nonzero values, the extents of rows, and column indices. It is similar to COO, but compresses the row indices, hence the name. This format allows fast row access and matrix-vector multiplications (Mx). The CSR format has been in use since at least the mid-1960s, with the first complete description appearing in 1967.
The CSR format stores a sparse m × n matrix M in row form using three (one-dimensional) arrays (A, IA, JA). Let NNZ denote the number of nonzero entries in M. (Note that zero-based indices shall be used here.)
- The array A is of length NNZ and holds all the nonzero entries of M in left-to-right top-to-bottom ("row-major") order.
- The array IA is of length m + 1. It is defined by this recursive definition:
- IA = 0
- IA[i] = IA[i − 1] + (number of nonzero elements on the (i-1)-th row in the original matrix)
- Thus, the i-th entry in IA stores the index of A at which the non-zero elements of the i-th row of M begin. Therefore, the values of the i-th row of the original matrix are read from the elements A[IA[i]] to A[IA[i + 1] − 1] (inclusive on both ends).
- Additionally, the last element IA[m+1] stores NNZ, the number of elements in A, which can be also thought of as the index in A of first element of a phantom row just beyond the end of the matrix M.
- The third array, JA, contains the column index in M of each element of A and hence is of length NNZ as well.
For example, the matrix
is a 4 × 4 matrix with 4 nonzero elements, hence
A = [ 5 8 3 6 ] IA = [ 0 0 2 3 4 ] JA = [ 0 1 2 1 ]
So, in array JA, the element "5" from A has column index 0, "8" and "6" have index 1, and element "3" has index 2.
In this case the CSR representation contains 13 entries, compared to 16 in the original matrix. The CSR format saves on memory only when NNZ < (m (n − 1) − 1) / 2. Another example, the matrix
is a 4 × 6 matrix (24 entries) with 8 nonzero elements, so
A = [ 10 20 30 40 50 60 70 80 ] IA = [ 0 2 4 7 8 ] JA = [ 0 1 1 3 2 3 4 5 ]
The whole is stored as 21 entries.
- IA splits the array A into rows:
(10, 20) (30, 40) (50, 60, 70) (80);
- JA aligns values in columns:
(10, 20, ...) (0, 30, 0, 40, ...)(0, 0, 50, 60, 70, 0) (0, 0, 0, 0, 0, 80).
Note that in this format, the first value of IA is always zero and the last is always NNZ, so they are in some sense redundant (although in programming languages where the array length needs to be explicitly stored, NNZ would not be redundant). Nonetheless, this does avoid the need to handle an exceptional case when computing the length of each row, as it guarantees the formula IA[i + 1] − IA[i] works for any row i. Moreover, the memory cost of this redundant storage is likely insignificant for a sufficiently large matrix.
The (old and new) Yale sparse matrix formats are instances of the CSR scheme. The old Yale format works exactly as described above, with three arrays; the new format combines IA and JA into a single array and handles the diagonal of the matrix separately.
It is likely known as the Yale format because it was proposed in the 1977 Yale Sparse Matrix Package report from Department of Computer Science at Yale University.
Compressed sparse column (CSC or CCS)
CSC is similar to CSR except that values are read first by column, a row index is stored for each value, and column pointers are stored. For example, CSC is (val, row_ind, col_ptr), where val is an array of the (top-to-bottom, then left-to-right) non-zero values of the matrix; row_ind is the row indices corresponding to the values; and, col_ptr is the list of val indexes where each column starts. The name is based on the fact that column index information is compressed relative to the COO format. One typically uses another format (LIL, DOK, COO) for construction. This format is efficient for arithmetic operations, column slicing, and matrix-vector products. See scipy.sparse.csc_matrix.
This is the traditional format for specifying a sparse matrix in MATLAB (via the
An important special type of sparse matrices is band matrix, defined as follows. The lower bandwidth of a matrix A is the smallest number p such that the entry ai,j vanishes whenever i > j + p. Similarly, the upper bandwidth is the smallest number p such that ai,j = 0 whenever i < j − p (Golub & Van Loan 1996, §1.2.1). For example, a tridiagonal matrix has lower bandwidth 1 and upper bandwidth 1. As another example, the following sparse matrix has lower and upper bandwidth both equal to 3. Notice that zeros are represented with dots for clarity.
Matrices with reasonably small upper and lower bandwidth are known as band matrices and often lend themselves to simpler algorithms than general sparse matrices; or one can sometimes apply dense matrix algorithms and gain efficiency simply by looping over a reduced number of indices.
By rearranging the rows and columns of a matrix A it may be possible to obtain a matrix A′ with a lower bandwidth. A number of algorithms are designed for bandwidth minimization.
A very efficient structure for an extreme case of band matrices, the diagonal matrix, is to store just the entries in the main diagonal as a one-dimensional array, so a diagonal n × n matrix requires only n entries.
The fill-in of a matrix are those entries that change from an initial zero to a non-zero value during the execution of an algorithm. To reduce the memory requirements and the number of arithmetic operations used during an algorithm, it is useful to minimize the fill-in by switching rows and columns in the matrix. The symbolic Cholesky decomposition can be used to calculate the worst possible fill-in before doing the actual Cholesky decomposition.
There are other methods than the Cholesky decomposition in use. Orthogonalization methods (such as QR factorization) are common, for example, when solving problems by least squares methods. While the theoretical fill-in is still the same, in practical terms the "false non-zeros" can be different for different methods. And symbolic versions of those algorithms can be used in the same manner as the symbolic Cholesky to compute worst case fill-in.
Solving sparse matrix equations
Both iterative and direct methods exist for sparse matrix solving.
Iterative methods, such as conjugate gradient method and GMRES utilize fast computations of matrix-vector products , where matrix is sparse. The use of preconditioners can significantly accelerate convergence of such iterative methods.
Several software libraries support sparse matrices, and provide solvers for sparse matrix equations. The following are open-source:
- SuiteSparse, a suite of sparse matrix algorithms
- ALGLIB is a C++ and C# library with sparse linear algebra support
- PETSc, a huge C library, contains many different matrix solvers.
- Eigen3 is a C++ library that contains several sparse matrix solvers. However, none of them are parallelized.
- MUMPS (MUltifrontal Massively Parallel sparse direct Solver), written in Fortran90, is a frontal solver
- Armadillo provides a user-friendly C++ wrapper for SuperLU
- SciPy provides support for several sparse matrix formats, linear algebra, and solvers
- SPArse Matrix (spam) R package for sparse matrices
- "Cerebras Systems Unveils the Industry's First Trillion Transistor Chip". www.businesswire.com. 2019-08-19. Retrieved 2019-12-02.
The WSE contains 400,000 AI-optimized compute cores. Called SLAC™ for Sparse Linear Algebra Cores, the compute cores are flexible, programmable, and optimized for the sparse linear algebra that underpins all neural network computation
- "Argonne National Laboratory Deploys Cerebras CS-1, the World's Fastest Artificial Intelligence Computer | Argonne National Laboratory". www.anl.gov (Press release). Retrieved 2019-12-02.
The WSE is the largest chip ever made at 46,225 square millimeters in area, it is 56.7 times larger than the largest graphics processing unit. It contains 78 times more AI optimized compute cores, 3,000 times more high speed, on-chip memory, 10,000 times more memory bandwidth, and 33,000 times more communication bandwidth.
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- Eisenstat, S. C.; Gursky, M. C.; Schultz, M. H.; Sherman, A. H. (April 1977). "Yale Sparse Matrix Package" (PDF). Retrieved 6 April 2019.
- pp. 9,10 in Oral history interview with Harry M. Markowitz
- Golub, Gene H.; Van Loan, Charles F. (1996). Matrix Computations (3rd ed.). Baltimore: Johns Hopkins. ISBN 978-0-8018-5414-9.
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- Bank, Randolph E.; Douglas, Craig C. "Sparse Matrix Multiplication Package" (PDF).
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- Gibbs, Norman E.; Poole, William G.; Stockmeyer, Paul K. (1976). "A comparison of several bandwidth and profile reduction algorithms". ACM Transactions on Mathematical Software. 2 (4): 322–330. doi:10.1145/355705.355707.
- Gilbert, John R.; Moler, Cleve; Schreiber, Robert (1992). "Sparse matrices in MATLAB: Design and Implementation". SIAM Journal on Matrix Analysis and Applications. 13 (1): 333–356. CiteSeerX 10.1.1.470.1054. doi:10.1137/0613024.
- Sparse Matrix Algorithms Research at the University of Florida, containing the UF sparse matrix collection.
- SMALL project A EU-funded project on sparse models, algorithms and dictionary learning for large-scale data.