SparseFactor(_:_:_:_:)
Returns the specified factorization of a sparse matrix of double-precision values using the specified options.
Declaration
func SparseFactor(_ type: SparseFactorization_t, _ Matrix: SparseMatrix_Double, _ options: SparseSymbolicFactorOptions, _ nfoptions: SparseNumericFactorOptions) -> SparseOpaqueFactorization_DoubleParameters
- type:
The type of factorization to perform.
- Matrix:
The matrix to factorize.
- options:
The symbolic factor options, such as the ordering algorithm to use.
- nfoptions:
The numeric factor options, such as the scaling method to use.
Return Value
A SparseOpaqueFactorization_Double structure that represents the matrix factorization.
Discussion
Use this function to calculate the factorization of a sparse matrix to pass to the direct solve functions. The following figure shows a system of equations where the coefficient matrix is sparse:
[Image]
The following code solves this system with a QR factorization of the coefficient matrix:
/// Create the coefficient matrix _A_.
let rowIndices: [Int32] = [ 0, 1, 1, 2]
let columnIndices: [Int32] = [ 2, 0, 2, 1]
let aValues: [Double] = [10, 20, 5, 50]
let A = SparseConvertFromCoordinate(3, 3,
4, 1,
SparseAttributes_t(),
rowIndices, columnIndices,
aValues)
/// Factorize _A_
let symbolicOptions = SparseSymbolicFactorOptions(
control: SparseDefaultControl,
orderMethod: SparseOrderDefault,
order: nil,
ignoreRowsAndColumns: nil,
malloc: { malloc($0) },
free: { free($0) },
reportError: nil)
let numericOptions = SparseNumericFactorOptions()
let factorization = SparseFactor(SparseFactorizationQR, A,
symbolicOptions,
numericOptions)
defer {
SparseCleanup(A)
SparseCleanup(factorization)
}
/// Create the right-hand-side vector, _b_.
var bValues = [30.0, 35.0, 100.0]
bValues.withUnsafeMutableBufferPointer { bPtr in
let xb = DenseVector_Double(count: 3,
data: bPtr.baseAddress!)
SparseSolve(factorization, xb)
}On return, bValues contains the values [1.0, 2.0, 3.0].
You can use the symbolic factorization that this function returns for multiple numerical factorizations with different numerical values but the same nonzero structure.