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ComplexSparseCholesky Class
Sparse Cholesky factorization of a matrix of type ComplexSparseMatrix.
Inheritance Hierarchy
SystemObject
  Imsl.MathComplexSparseCholesky

Namespace: Imsl.Math
Assembly: ImslCS (in ImslCS.dll) Version: 6.5.2.0
Syntax
[SerializableAttribute]
public class ComplexSparseCholesky

The ComplexSparseCholesky type exposes the following members.

Constructors
  NameDescription
Public methodComplexSparseCholesky
Constructs the matrix structure for the Cholesky factorization of a sparse Hermitian positive definite matrix of type ComplexSparseMatrix.
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Methods
  NameDescription
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Public methodFactorNumerically
Computes the numeric factorization of a sparse Hermitian positive definite matrix.
Public methodFactorSymbolically
Computes the symbolic factorization of a sparse Hermitian positive definite matrix.
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetHashCode
Serves as a hash function for a particular type.
(Inherited from Object.)
Public methodGetNumericFactor
Returns the numeric Cholesky factor.
Public methodGetSymbolicFactor
Returns the symbolic Cholesky factor.
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodSetNumericFactor
Sets the numeric Cholesky factor to use in solving a sparse complex Hermitian positive definite system of linear equations Ax=b.
Public methodSetSymbolicFactor
Sets the symbolic Cholesky factor to use in solving a sparse complex Hermitian positive definite system of linear equations Ax=b.
Public methodSolve
Computes the solution of a sparse Hermitian positive definite system of linear equations Ax=b.
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
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Properties
  NameDescription
Public propertyLargestDiagonalElement
The largest diagonal element of the Cholesky factor.
Public propertyNumberOfNonzeros
The number of nonzeros in the Cholesky factor.
Public propertyNumericFactorizationMethod
The method used in the numerical factorization of the permuted input matrix.
Public propertySmallestDiagonalElement
The smallest diagonal element of the Cholesky factor.
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Remarks

Class ComplexSparseCholesky computes the Cholesky factorization of a sparse Hermitian positive definite matrix A. This factorization can then be used to compute the solution of the linear system Ax = b.

Typically, the solution of a large sparse positive definite system Ax = b is done in four steps.

  1. In step one, an ordering algorithm is used to preserve sparsity in the Cholesky factor L of matrix A during the numerical factorization process. The new order can be described by a permutation matrix P.
  2. Step two consists of setting up the data structure for the Cholesky factor L, where PAP^T=LL^T. This step is called the symbolic factorization phase of the computation. During symbolic factorization, only the sparsity pattern of sparse matrix A, i.e., the locations of the nonzero entries of matrix A are needed but not any of the elements themselves.
  3. In step 3, the numerical factorization phase, the Cholesky factorization is done numerically.
  4. Step 4 is the solution phase. Here, the numerical solution, x, to the original system is obtained by solving the two triangular systems Ly_1=Pb, L^Ty_2=y_1 and the permutation x=P^Ty_2.

Class ComplexSparseCholesky realizes all four steps by algorithms described in George and Liu (1981). Especially, step one, is a realization of a minimum degree ordering algorithm. The numerical factorization in its standard form is based on a sparse compressed storage scheme. Alternatively, a multifrontal method can be used. The multifrontal method requires more storage but will be faster than the standard method in certain cases. The multifrontal method is based on the routines in Liu (1987). For a detailed description of this method, see Liu (1990), also Duff and Reid(1983, 1984), Ashcraft (1987) et al. (1987), and Liu (1986, 1989, 1992). The numerical factorization method can be specified by using the NumericFactorizationMethod property.

The Solve method will compute the symbolic and numeric factorizations if they have not already been computed or supplied by the user through the FactorSymbolically, FactorNumerically, SetNumericFactor, or SetSymbolicFactor methods. These factorizations are retained for later use by the Solve method when different right-hand sides are to be solved.

There is a special situation where computations can be simplified.If an application generates different sparse Hermitian positive definite coefficient matrices that all have the same sparsity pattern, then by using methods GetSymbolicFactor and SetSymbolicFactor the symbolic factorization needs only be computed once.

See Also

Reference

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