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{| class=wikitable align=right width=240px style="margin:3px 15px 5px 0;"
| <center>'''Example of sparse matrix'''</center>
<small>
<code>
&nbsp; [      11      22 &nbsp;0 &nbsp;0 &nbsp;0 &nbsp;0 &nbsp;0 ]<br>
&nbsp; [ &nbsp;0      33      44 &nbsp;0 &nbsp;0 &nbsp;0 &nbsp;0 ]<br>
&nbsp; [ &nbsp;0 &nbsp;0      55      66      77 &nbsp;0 &nbsp;0 ]<br>
&nbsp; [ &nbsp;0 &nbsp;0 &nbsp;0 &nbsp;0 &nbsp;0      88 &nbsp;0 ]<br>
&nbsp; [ &nbsp;0 &nbsp;0 &nbsp;0 &nbsp;0 &nbsp;0 &nbsp;0      99 ]<br>
</code>
</small>
|-
| <center>{{midsize|The above sparse matrix contains<br>only 9 nonzero elements of the 35, <br>with&nbsp;26 of those elements as zero.}}</center>
|}
[[Image:Finite element sparse matrix.png|right|thumb|A sparse matrix obtained when solving a [[finite element method|finite element problem]] in two dimensions. The non-zero elements are shown in black.]]
In the subfield of [[numerical analysis]], a '''sparse matrix''' is a [[matrix (mathematics)|matrix]] populated primarily with zeros {{harv|Stoer|Bulirsch|2002|p=619}} as elements of the table.
By contrast, if a larger number of elements differ from zero, then it is common to refer to the matrix as a '''dense matrix'''.
The fraction of zero elements (non-zero elements) in a matrix is called the '''sparsity''' ('''density''').
 
Conceptually, sparsity corresponds to systems which are loosely coupled. Consider a line of balls connected by springs from one to the next; this is a sparse system. By contrast, if the same line of balls had springs connecting each ball to all other balls, the system would be represented by 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.
 
Huge sparse matrices often appear in [[science]] or [[engineering]] when solving [[partial differential equation]]s.
 
When storing and manipulating sparse matrices on a [[computer]], it is beneficial and often necessary to use specialized [[algorithm]]s and [[data structure]]s that take advantage of the sparse structure of the matrix. Operations using standard dense-matrix structures and algorithms are relatively slow and consume large amounts of [[Computer memory|memory]] when applied to large sparse matrices. Sparse data is by nature easily [[data compression|compressed]], and this compression almost always results in significantly less [[computer data storage]] usage. Indeed, some very large sparse matrices are infeasible to manipulate using standard dense algorithms.
 
=={{anchor|storage}} Storing a sparse matrix==
The basic data structure for a matrix is a two-dimensional array. Each entry in the array represents an element ''a''<sub>''i'',''j''</sub> of the matrix and can be accessed by the two [[Array data structure|indices]] ''i''&nbsp; and ''j''. Traditionally, ''i''&nbsp; indicates the row number (top-to-bottom), while ''j''&nbsp; indicates the column number (left-to-right) of each element in the table. For an ''m''×''n'' matrix,  enough memory to store up to (''m''×''n'') entries to represent the matrix is needed.
 
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 [[Computer storage|memory]] when compared to the basic approach.
 
Formats can be divided into two groups: (1) those that support efficient modification, and (2) those that support efficient matrix operations.  The efficient modification group includes DOK (Dictionary of keys), LIL (List of lists), and COO (Coordinate list) and is typically used to construct the matrix.  Once the matrix is constructed, it is typically converted to a format, such as CSR (Compressed Sparse Row) or CSC (Compressed Sparse Column), which is more efficient for matrix operations.
 
===Dictionary of keys (DOK)===
 
DOK represents non-zero values as a dictionary (e.g., a [[hash table]] or [[binary search tree]]) mapping <code>(row, column)</code>-[[ordered pair|pairs]] to values.  This format is good for incrementally constructing a sparse array, but poor for iterating over non-zero values in sorted order.  One typically creates the matrix with this format, then converts to another format for processing
 
===List of lists (LIL)===
 
LIL stores one list per row, where each entry stores a column index and value.  Typically, these entries are kept sorted by column index for faster lookup.  This is another format which is good for incremental matrix construction.  See [http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.lil_matrix.html scipy.sparse.lil_matrix].
 
===Coordinate list (COO)===
 
COO stores a list of <code>(row, column, value)</code> tuples.  Ideally, the entries are sorted (by row index, then column index) to improve random access times.  This is another format which is good for incremental matrix construction. See [http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.coo_matrix.html scipy.sparse.coo_matrix].
 
===Yale format===
The Yale Sparse Matrix Format stores an initial sparse ''m''×''n'' matrix, ''M'', in row form using three one-dimensional arrays. Let <code>NNZ</code> denote the number of nonzero entries of ''M''. The first array is <code>A</code>, which is of length <code>NNZ</code>, and holds all nonzero entries of ''M'' in left-to-right top-to-bottom (row-major) order. The second array is <code>IA</code>, which is of length <math>m + 1</math> (i.e., one entry per row, plus one). <code>IA(i)</code> contains the index in <code>A</code> of the first nonzero element of row <code>i</code>. Row <code>i</code> of the original matrix extends from <code>A(IA(i))</code> to <code>A(IA(i+1)-1)</code>, i.e. from the start of one row to the last index before the start of the next. The third array, <code>JA</code>, contains the column index (zero-based) of each element of A, so it also is of length <code>NNZ</code>.
For example, the matrix
:::<code> [ 1 2 0 0 ]</code>
:::<code> [ 0 3 9 0 ]</code>
:::<code> [ 0 1 4 0 ]</code>
 
is a three-by-four matrix with six nonzero elements, so
:::<code> A  = [ 1 2 3 9 1 4 ]</code> &nbsp; (array of non-zero element values)
:::<code> IA = [ 0 2 4 6 ]</code> &nbsp; (array of index of first nonzero element of row <code>i</code>)
:::<code> JA = [ 0 1 1 2 1 2 ]</code> &nbsp; (array of column index of each <code>A</code> element)
So, in array <code>JA</code>, the element "1" from <code>A</code> has column index 0, "2" and "3" have index 1, "9" has index 2, the second element "1" has column index 1, and element "4" has index 2. Typically, mathematicians would number the columns 1-4, while some computer systems use index 0-3.
 
In this case the Yale representation contains 16 entries, compared to only 12 in the original matrix. The Yale format saves on memory only when <math>NNZ < (m(n-1) - 1)/2</math>.
Another example, the matrix
        [ 10 20  0  0  0  0 ]
        [  0 30  0 40  0  0 ]
        [  0  0 50 60 70  0 ]
        [  0  0  0  0  0 80 ]
 
is a four-by-six matrix (24 entries) with eight nonzero elements, so (spacing elements to be aligned with concerned data)
        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.
:* <code>IA</code> splits the array <code>A</code> into rows: <code>(10,20)(30,40)(50,60,70)(80)</code>;
:* <code>JA</code> aligns values in columns: <code>(10,20,...)(0,30,0,40,...)(0,0,50,60,70,0)(0,0,0,0,0,80)</code>.
 
(Note that in this format, the first value of IA will always be zero and the last will always be NNZ: these two cells may not be useful.)
 
===Compressed sparse row (CSR or CRS)===
 
[http://netlib.org/linalg/html_templates/node91.html#SECTION00931100000000000000 CSR] is effectively identical to the Yale Sparse Matrix format, except that the column array is normally stored ahead of the row index array. I.e. CSR is <code>(val, col_ind, row_ptr)</code>, where <code>val</code> is an array of the (left-to-right, then top-to-bottom) non-zero values of the matrix; <code>col_ind</code> is the column indices corresponding to the values; and, <code>row_ptr</code> is the list of value indexes where each row starts.  The name is based on the fact that row 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, row slicing, and matrix-vector products.  See [http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html scipy.sparse.csr_matrix].
 
===Compressed sparse column (CSC or CCS)===
 
[http://netlib.org/linalg/html_templates/node92.html#SECTION00931200000000000000 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.  I.e. CSC is <code>(val, row_ind, col_ptr)</code>, where <code>val</code> is an array of the (top-to-bottom, then left-to-right-bottom) non-zero values of the matrix; <code>row_ind</code> is the row indices corresponding to the values; and, <code>col_ptr</code> is the list of <code>val</code> 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 [http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csc_matrix.html scipy.sparse.csc_matrix].
This is the traditional format for specifying a sparse matrix in MATLAB (via the <code>sparse</code> function).
 
==Special structure==
 
===Banded===
{{main|Band matrix}}
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 ''a''<sub>''ij''</sub> vanishes whenever ''i'' > ''j'' + p. Similarly, the ''upper bandwidth'' is the smallest ''p'' such that ''a''<sub>''ij''</sub> = 0 whenever ''i'' < ''j'' &minus; ''p'' {{harv|Golub|Van Loan|1996|loc=§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.
::<math>
\left(
\begin{smallmatrix}
  X  &  X  &  X  & \cdot & \cdot & \cdot & \cdot & \\
  X  &  X  & \cdot &  X  &  X  & \cdot & \cdot & \\
  X  & \cdot &  X  & \cdot &  X  & \cdot & \cdot & \\
\cdot &  X  & \cdot &  X  & \cdot &  X  & \cdot & \\
\cdot &  X  &  X  & \cdot &  X  &  X  &  X  & \\
\cdot & \cdot & \cdot &  X  &  X  &  X  & \cdot & \\     
\cdot & \cdot & \cdot & \cdot &  X  & \cdot &  X  & \\             
\end{smallmatrix}
\right)
</math>
 
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 [[Graph bandwidth|bandwidth minimization]].
 
===Diagonal===
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.
 
===Symmetric===
A symmetric sparse matrix arises as the [[adjacency matrix]] of an [[undirected graph]]; it can be stored efficiently as an [[adjacency list]].
 
==Reducing fill-in==
 
The '''fill-in''' of a matrix are those entries which 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 method|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 <math>Ax_i</math>, where matrix <math>A</math> is sparse. The use of [[preconditioner | preconditioners]] can significantly accelerate convergence of such iterative methods.
 
==See also==
{|
| valign=top |
* [[Matrix representation]]{{nb10}}
* [[Pareto principle]]
* [[Ragged matrix]]
| valign=top |
* [[Skyline matrix]]{{nb10}}{{nb10}}
* [[Sparse array]]
* [[Sparse graph code]]
| valign=top |
* [[Sparse file]]
* [[Harwell-Boeing file format]]
* [[Matrix Market exchange formats]]
|}
 
==References==
* {{Cite book | first1=Gene H. | last1=Golub | author1-link=Gene H. Golub | first2=Charles F. | last2=Van Loan | author2-link=Charles F. Van Loan | year=1996 | title=Matrix Computations | edition=3rd | publisher=Johns Hopkins | place=Baltimore | isbn=978-0-8018-5414-9 }}
* {{Cite book | last1=Stoer | first1=Josef | last2=Bulirsch | first2=Roland | title=Introduction to Numerical Analysis | publisher=[[Springer-Verlag]] | location=Berlin, New York | edition=3rd | isbn=978-0-387-95452-3 | year=2002}}
* {{Cite book | last=Tewarson| first=Reginald P.|title=Sparse Matrices (Part of the Mathematics in Science & Engineering series)|publisher= Academic Press Inc.|date=May 1973}} (This book, by a professor at the State University of New York at Stony Book, was the first book exclusively dedicated to Sparse Matrices.  Graduate courses using this as a textbook were offered at that University in the early 1980s).
* {{Cite web |title=Sparse Matrix Multiplication Package|first1= Randolph E.|last1= Bank|first2= Craig C.|last2= Douglas |url=http://www.mgnet.org/~douglas/Preprints/pub0034.pdf}}
* {{Cite book |last=Pissanetzky|first= Sergio|year= 1984|title=Sparse Matrix Technology|publisher= Academic Press}}
*{{cite journal|doi=10.1007/BF02521587|title=Reducing the profile of sparse symmetric matrices|year=1976|last1=Snay|first1=Richard A.|journal=Bulletin Géodésique|volume=50|issue=4|pages=341}} Also NOAA Technical Memorandum NOS NGS-4, National Geodetic Survey, Rockville, MD.
 
==Further reading==
* {{cite journal | title = A comparison of several bandwidth and profile reduction algorithms | journal = ACM Transactions on Mathematical Software | year = 1976 | volume = 2 | issue = 4 | pages = 322–330 | url = http://portal.acm.org/citation.cfm?id=355707 | doi = 10.1145/355705.355707 | last1 = Gibbs | first1 = Norman E. | last2 = Poole | first2 = William G. | last3 = Stockmeyer | first3 = Paul K.  }}
* {{cite journal | title = Sparse matrices in MATLAB: Design and Implementation | journal = SIAM Journal on Matrix Analysis and Applications | year = 1992 | volume = 13 | issue = 1 | pages = 333–356 | url = http://citeseer.ist.psu.edu/gilbert91sparse.html | doi = 10.1137/0613024 | last1 = Gilbert | first1 = John R. | last2 = Moler | first2 = Cleve | last3 = Schreiber | first3 = Robert }}
* [http://www.cise.ufl.edu/research/sparse Sparse Matrix Algorithms Research] at the University of Florida, containing the UF sparse matrix collection.
* [http://www.small-project.eu SMALL project] A EU-funded project on sparse models, algorithms and dictionary learning for large-scale data.
 
==External links==
*[http://www.solvingequations.net Equations Solver Online]
*[http://purl.umn.edu/107467 Oral history interview with Harry M. Markowitz],  [[Charles Babbage Institute]], University of Minnesota.  [[Harry Markowitz|Markowitz]] discusses his development of [[portfolio theory]] (for which he received a Nobel Prize in Economics), '''sparse matrix methods''', and his work at the [[RAND Corporation]] and elsewhere on simulation software development (including computer language [[SIMSCRIPT]]), modeling, and operations research.
 
{{DEFAULTSORT:Sparse Matrix}}
[[Category:Sparse matrices]]

Revision as of 14:03, 7 January 2014

Example of sparse matrix

  [ 11 22  0  0  0  0  0 ]
  [  0 33 44  0  0  0  0 ]
  [  0  0 55 66 77  0  0 ]
  [  0  0  0  0  0 88  0 ]
  [  0  0  0  0  0  0 99 ]

Template:Midsize
A sparse matrix obtained when solving a finite element problem in two dimensions. The non-zero elements are shown in black.

In the subfield of numerical analysis, a sparse matrix is a matrix populated primarily with zeros Template:Harv as elements of the table. By contrast, if a larger number of elements differ from zero, then it is common to refer to the matrix as a dense matrix. The fraction of zero elements (non-zero elements) in a matrix is called the sparsity (density).

Conceptually, sparsity corresponds to systems which are loosely coupled. Consider a line of balls connected by springs from one to the next; this is a sparse system. By contrast, if the same line of balls had springs connecting each ball to all other balls, the system would be represented by 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.

Huge sparse matrices often appear in science or engineering when solving partial differential equations.

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. Operations using standard dense-matrix structures and algorithms are relatively slow and consume large amounts of memory when applied to large sparse matrices. Sparse data is by nature easily compressed, and this compression almost always results in significantly less computer data storage usage. Indeed, some very large sparse matrices are infeasible to manipulate using standard dense algorithms.

<storage>...</storage> Storing a sparse matrix

The basic data structure for a matrix is a two-dimensional array. Each entry in the array represents an element ai,j of the matrix and can be accessed by the two indices i  and j. Traditionally, i  indicates the row number (top-to-bottom), while j  indicates the column number (left-to-right) of each element in the table. For an m×n matrix, enough memory to store up to (m×n) entries to represent the matrix is needed.

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.

Formats can be divided into two groups: (1) those that support efficient modification, and (2) those that support efficient matrix operations. The efficient modification group includes DOK (Dictionary of keys), LIL (List of lists), and COO (Coordinate list) and is typically used to construct the matrix. Once the matrix is constructed, it is typically converted to a format, such as CSR (Compressed Sparse Row) or CSC (Compressed Sparse Column), which is more efficient for matrix operations.

Dictionary of keys (DOK)

DOK represents non-zero values as a dictionary (e.g., a hash table or binary search tree) mapping (row, column)-pairs to values. This format is good for incrementally constructing a sparse array, but poor for iterating over non-zero values in sorted order. One typically creates the matrix with this format, then converts to another format for processing

List of lists (LIL)

LIL stores one list per row, where each entry stores a column index and value. Typically, these entries are kept sorted by column index for faster lookup. This is another format which is good for incremental matrix construction. See scipy.sparse.lil_matrix.

Coordinate list (COO)

COO stores a list of (row, column, value) tuples. Ideally, the entries are sorted (by row index, then column index) to improve random access times. This is another format which is good for incremental matrix construction. See scipy.sparse.coo_matrix.

Yale format

The Yale Sparse Matrix Format stores an initial sparse m×n matrix, M, in row form using three one-dimensional arrays. Let NNZ denote the number of nonzero entries of M. The first array is A, which is of length NNZ, and holds all nonzero entries of M in left-to-right top-to-bottom (row-major) order. The second array is IA, which is of length (i.e., one entry per row, plus one). IA(i) contains the index in A of the first nonzero element of row i. Row i of the original matrix extends from A(IA(i)) to A(IA(i+1)-1), i.e. from the start of one row to the last index before the start of the next. The third array, JA, contains the column index (zero-based) of each element of A, so it also is of length NNZ. For example, the matrix

[ 1 2 0 0 ]
[ 0 3 9 0 ]
[ 0 1 4 0 ]

is a three-by-four matrix with six nonzero elements, so

A = [ 1 2 3 9 1 4 ]   (array of non-zero element values)
IA = [ 0 2 4 6 ]   (array of index of first nonzero element of row i)
JA = [ 0 1 1 2 1 2 ]   (array of column index of each A element)

So, in array JA, the element "1" from A has column index 0, "2" and "3" have index 1, "9" has index 2, the second element "1" has column index 1, and element "4" has index 2. Typically, mathematicians would number the columns 1-4, while some computer systems use index 0-3.

In this case the Yale representation contains 16 entries, compared to only 12 in the original matrix. The Yale format saves on memory only when . Another example, the matrix

        [ 10 20  0  0  0  0 ]
        [  0 30  0 40  0  0 ]
        [  0  0 50 60 70  0 ]
        [  0  0  0  0  0 80 ]

is a four-by-six matrix (24 entries) with eight nonzero elements, so (spacing elements to be aligned with concerned data)

        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 will always be zero and the last will always be NNZ: these two cells may not be useful.)

Compressed sparse row (CSR or CRS)

CSR is effectively identical to the Yale Sparse Matrix format, except that the column array is normally stored ahead of the row index array. I.e. CSR is (val, col_ind, row_ptr), where val is an array of the (left-to-right, then top-to-bottom) non-zero values of the matrix; col_ind is the column indices corresponding to the values; and, row_ptr is the list of value indexes where each row starts. The name is based on the fact that row 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, row slicing, and matrix-vector products. See scipy.sparse.csr_matrix.

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. I.e. CSC is (val, row_ind, col_ptr), where val is an array of the (top-to-bottom, then left-to-right-bottom) 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 sparse function).

Special structure

Banded

Mining Engineer (Excluding Oil ) Truman from Alma, loves to spend time knotting, largest property developers in singapore developers in singapore and stamp collecting. Recently had a family visit to Urnes Stave Church. 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 aij vanishes whenever i > j + p. Similarly, the upper bandwidth is the smallest p such that aij = 0 whenever i < jp Template:Harv. 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.

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.

Diagonal

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.

Symmetric

A symmetric sparse matrix arises as the adjacency matrix of an undirected graph; it can be stored efficiently as an adjacency list.

Reducing fill-in

The fill-in of a matrix are those entries which 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.

See also

References

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  • 20 year-old Real Estate Agent Rusty from Saint-Paul, has hobbies and interests which includes monopoly, property developers in singapore and poker. Will soon undertake a contiki trip that may include going to the Lower Valley of the Omo.

    My blog: http://www.primaboinca.com/view_profile.php?userid=5889534
  • 20 year-old Real Estate Agent Rusty from Saint-Paul, has hobbies and interests which includes monopoly, property developers in singapore and poker. Will soon undertake a contiki trip that may include going to the Lower Valley of the Omo.

    My blog: http://www.primaboinca.com/view_profile.php?userid=5889534 (This book, by a professor at the State University of New York at Stony Book, was the first book exclusively dedicated to Sparse Matrices. Graduate courses using this as a textbook were offered at that University in the early 1980s).
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  • 20 year-old Real Estate Agent Rusty from Saint-Paul, has hobbies and interests which includes monopoly, property developers in singapore and poker. Will soon undertake a contiki trip that may include going to the Lower Valley of the Omo.

    My blog: http://www.primaboinca.com/view_profile.php?userid=5889534
  • One of the biggest reasons investing in a Singapore new launch is an effective things is as a result of it is doable to be lent massive quantities of money at very low interest rates that you should utilize to purchase it. Then, if property values continue to go up, then you'll get a really high return on funding (ROI). Simply make sure you purchase one of the higher properties, reminiscent of the ones at Fernvale the Riverbank or any Singapore landed property Get Earnings by means of Renting

    In its statement, the singapore property listing - website link, government claimed that the majority citizens buying their first residence won't be hurt by the new measures. Some concessions can even be prolonged to chose teams of consumers, similar to married couples with a minimum of one Singaporean partner who are purchasing their second property so long as they intend to promote their first residential property. Lower the LTV limit on housing loans granted by monetary establishments regulated by MAS from 70% to 60% for property purchasers who are individuals with a number of outstanding housing loans on the time of the brand new housing purchase. Singapore Property Measures - 30 August 2010 The most popular seek for the number of bedrooms in Singapore is 4, followed by 2 and three. Lush Acres EC @ Sengkang

    Discover out more about real estate funding in the area, together with info on international funding incentives and property possession. Many Singaporeans have been investing in property across the causeway in recent years, attracted by comparatively low prices. However, those who need to exit their investments quickly are likely to face significant challenges when trying to sell their property – and could finally be stuck with a property they can't sell. Career improvement programmes, in-house valuation, auctions and administrative help, venture advertising and marketing, skilled talks and traisning are continuously planned for the sales associates to help them obtain better outcomes for his or her shoppers while at Knight Frank Singapore. No change Present Rules

    Extending the tax exemption would help. The exemption, which may be as a lot as $2 million per family, covers individuals who negotiate a principal reduction on their existing mortgage, sell their house short (i.e., for lower than the excellent loans), or take part in a foreclosure course of. An extension of theexemption would seem like a common-sense means to assist stabilize the housing market, but the political turmoil around the fiscal-cliff negotiations means widespread sense could not win out. Home Minority Chief Nancy Pelosi (D-Calif.) believes that the mortgage relief provision will be on the table during the grand-cut price talks, in response to communications director Nadeam Elshami. Buying or promoting of blue mild bulbs is unlawful.

    A vendor's stamp duty has been launched on industrial property for the primary time, at rates ranging from 5 per cent to 15 per cent. The Authorities might be trying to reassure the market that they aren't in opposition to foreigners and PRs investing in Singapore's property market. They imposed these measures because of extenuating components available in the market." The sale of new dual-key EC models will even be restricted to multi-generational households only. The models have two separate entrances, permitting grandparents, for example, to dwell separately. The vendor's stamp obligation takes effect right this moment and applies to industrial property and plots which might be offered inside three years of the date of buy. JLL named Best Performing Property Brand for second year running

    The data offered is for normal info purposes only and isn't supposed to be personalised investment or monetary advice. Motley Fool Singapore contributor Stanley Lim would not personal shares in any corporations talked about. Singapore private home costs increased by 1.eight% within the fourth quarter of 2012, up from 0.6% within the earlier quarter. Resale prices of government-built HDB residences which are usually bought by Singaporeans, elevated by 2.5%, quarter on quarter, the quickest acquire in five quarters. And industrial property, prices are actually double the levels of three years ago. No withholding tax in the event you sell your property. All your local information regarding vital HDB policies, condominium launches, land growth, commercial property and more

    There are various methods to go about discovering the precise property. Some local newspapers (together with the Straits Instances ) have categorised property sections and many local property brokers have websites. Now there are some specifics to consider when buying a 'new launch' rental. Intended use of the unit Every sale begins with 10 p.c low cost for finish of season sale; changes to 20 % discount storewide; follows by additional reduction of fiftyand ends with last discount of 70 % or extra. Typically there is even a warehouse sale or transferring out sale with huge mark-down of costs for stock clearance. Deborah Regulation from Expat Realtor shares her property market update, plus prime rental residences and houses at the moment available to lease Esparina EC @ Sengkang Also NOAA Technical Memorandum NOS NGS-4, National Geodetic Survey, Rockville, MD.

Further reading

  • One of the biggest reasons investing in a Singapore new launch is an effective things is as a result of it is doable to be lent massive quantities of money at very low interest rates that you should utilize to purchase it. Then, if property values continue to go up, then you'll get a really high return on funding (ROI). Simply make sure you purchase one of the higher properties, reminiscent of the ones at Fernvale the Riverbank or any Singapore landed property Get Earnings by means of Renting

    In its statement, the singapore property listing - website link, government claimed that the majority citizens buying their first residence won't be hurt by the new measures. Some concessions can even be prolonged to chose teams of consumers, similar to married couples with a minimum of one Singaporean partner who are purchasing their second property so long as they intend to promote their first residential property. Lower the LTV limit on housing loans granted by monetary establishments regulated by MAS from 70% to 60% for property purchasers who are individuals with a number of outstanding housing loans on the time of the brand new housing purchase. Singapore Property Measures - 30 August 2010 The most popular seek for the number of bedrooms in Singapore is 4, followed by 2 and three. Lush Acres EC @ Sengkang

    Discover out more about real estate funding in the area, together with info on international funding incentives and property possession. Many Singaporeans have been investing in property across the causeway in recent years, attracted by comparatively low prices. However, those who need to exit their investments quickly are likely to face significant challenges when trying to sell their property – and could finally be stuck with a property they can't sell. Career improvement programmes, in-house valuation, auctions and administrative help, venture advertising and marketing, skilled talks and traisning are continuously planned for the sales associates to help them obtain better outcomes for his or her shoppers while at Knight Frank Singapore. No change Present Rules

    Extending the tax exemption would help. The exemption, which may be as a lot as $2 million per family, covers individuals who negotiate a principal reduction on their existing mortgage, sell their house short (i.e., for lower than the excellent loans), or take part in a foreclosure course of. An extension of theexemption would seem like a common-sense means to assist stabilize the housing market, but the political turmoil around the fiscal-cliff negotiations means widespread sense could not win out. Home Minority Chief Nancy Pelosi (D-Calif.) believes that the mortgage relief provision will be on the table during the grand-cut price talks, in response to communications director Nadeam Elshami. Buying or promoting of blue mild bulbs is unlawful.

    A vendor's stamp duty has been launched on industrial property for the primary time, at rates ranging from 5 per cent to 15 per cent. The Authorities might be trying to reassure the market that they aren't in opposition to foreigners and PRs investing in Singapore's property market. They imposed these measures because of extenuating components available in the market." The sale of new dual-key EC models will even be restricted to multi-generational households only. The models have two separate entrances, permitting grandparents, for example, to dwell separately. The vendor's stamp obligation takes effect right this moment and applies to industrial property and plots which might be offered inside three years of the date of buy. JLL named Best Performing Property Brand for second year running

    The data offered is for normal info purposes only and isn't supposed to be personalised investment or monetary advice. Motley Fool Singapore contributor Stanley Lim would not personal shares in any corporations talked about. Singapore private home costs increased by 1.eight% within the fourth quarter of 2012, up from 0.6% within the earlier quarter. Resale prices of government-built HDB residences which are usually bought by Singaporeans, elevated by 2.5%, quarter on quarter, the quickest acquire in five quarters. And industrial property, prices are actually double the levels of three years ago. No withholding tax in the event you sell your property. All your local information regarding vital HDB policies, condominium launches, land growth, commercial property and more

    There are various methods to go about discovering the precise property. Some local newspapers (together with the Straits Instances ) have categorised property sections and many local property brokers have websites. Now there are some specifics to consider when buying a 'new launch' rental. Intended use of the unit Every sale begins with 10 p.c low cost for finish of season sale; changes to 20 % discount storewide; follows by additional reduction of fiftyand ends with last discount of 70 % or extra. Typically there is even a warehouse sale or transferring out sale with huge mark-down of costs for stock clearance. Deborah Regulation from Expat Realtor shares her property market update, plus prime rental residences and houses at the moment available to lease Esparina EC @ Sengkang
  • One of the biggest reasons investing in a Singapore new launch is an effective things is as a result of it is doable to be lent massive quantities of money at very low interest rates that you should utilize to purchase it. Then, if property values continue to go up, then you'll get a really high return on funding (ROI). Simply make sure you purchase one of the higher properties, reminiscent of the ones at Fernvale the Riverbank or any Singapore landed property Get Earnings by means of Renting

    In its statement, the singapore property listing - website link, government claimed that the majority citizens buying their first residence won't be hurt by the new measures. Some concessions can even be prolonged to chose teams of consumers, similar to married couples with a minimum of one Singaporean partner who are purchasing their second property so long as they intend to promote their first residential property. Lower the LTV limit on housing loans granted by monetary establishments regulated by MAS from 70% to 60% for property purchasers who are individuals with a number of outstanding housing loans on the time of the brand new housing purchase. Singapore Property Measures - 30 August 2010 The most popular seek for the number of bedrooms in Singapore is 4, followed by 2 and three. Lush Acres EC @ Sengkang

    Discover out more about real estate funding in the area, together with info on international funding incentives and property possession. Many Singaporeans have been investing in property across the causeway in recent years, attracted by comparatively low prices. However, those who need to exit their investments quickly are likely to face significant challenges when trying to sell their property – and could finally be stuck with a property they can't sell. Career improvement programmes, in-house valuation, auctions and administrative help, venture advertising and marketing, skilled talks and traisning are continuously planned for the sales associates to help them obtain better outcomes for his or her shoppers while at Knight Frank Singapore. No change Present Rules

    Extending the tax exemption would help. The exemption, which may be as a lot as $2 million per family, covers individuals who negotiate a principal reduction on their existing mortgage, sell their house short (i.e., for lower than the excellent loans), or take part in a foreclosure course of. An extension of theexemption would seem like a common-sense means to assist stabilize the housing market, but the political turmoil around the fiscal-cliff negotiations means widespread sense could not win out. Home Minority Chief Nancy Pelosi (D-Calif.) believes that the mortgage relief provision will be on the table during the grand-cut price talks, in response to communications director Nadeam Elshami. Buying or promoting of blue mild bulbs is unlawful.

    A vendor's stamp duty has been launched on industrial property for the primary time, at rates ranging from 5 per cent to 15 per cent. The Authorities might be trying to reassure the market that they aren't in opposition to foreigners and PRs investing in Singapore's property market. They imposed these measures because of extenuating components available in the market." The sale of new dual-key EC models will even be restricted to multi-generational households only. The models have two separate entrances, permitting grandparents, for example, to dwell separately. The vendor's stamp obligation takes effect right this moment and applies to industrial property and plots which might be offered inside three years of the date of buy. JLL named Best Performing Property Brand for second year running

    The data offered is for normal info purposes only and isn't supposed to be personalised investment or monetary advice. Motley Fool Singapore contributor Stanley Lim would not personal shares in any corporations talked about. Singapore private home costs increased by 1.eight% within the fourth quarter of 2012, up from 0.6% within the earlier quarter. Resale prices of government-built HDB residences which are usually bought by Singaporeans, elevated by 2.5%, quarter on quarter, the quickest acquire in five quarters. And industrial property, prices are actually double the levels of three years ago. No withholding tax in the event you sell your property. All your local information regarding vital HDB policies, condominium launches, land growth, commercial property and more

    There are various methods to go about discovering the precise property. Some local newspapers (together with the Straits Instances ) have categorised property sections and many local property brokers have websites. Now there are some specifics to consider when buying a 'new launch' rental. Intended use of the unit Every sale begins with 10 p.c low cost for finish of season sale; changes to 20 % discount storewide; follows by additional reduction of fiftyand ends with last discount of 70 % or extra. Typically there is even a warehouse sale or transferring out sale with huge mark-down of costs for stock clearance. Deborah Regulation from Expat Realtor shares her property market update, plus prime rental residences and houses at the moment available to lease Esparina EC @ Sengkang
  • 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.

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