SparseSolve(_:_:_:_:)
Solves the equation AX = B for matrices of double-precision values using the specified iterative method.
Declaration
func SparseSolve(_ method: SparseIterativeMethod, _ A: SparseMatrix_Double, _ B: DenseMatrix_Double, _ X: DenseMatrix_Double) -> SparseIterativeStatus_tParameters
- method:
The iterative method.
- A:
The matrix A.
- B:
The matrix B.
- X:
The matrix X.
Return Value
A SparseIterativeStatus_t enumeration that represents the status of the iterative solve.
Discussion
Use this function to solve a system of linear equations using a factored coefficient matrix. The following figure shows two systems of equations where the coefficient matrix is sparse:
[Image]
The following code solves this system using the least squares minimum residual method:
/// 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)
defer {
SparseCleanup(A)
}
/// Create the right-hand-side matrix, _B_.
var bValues: [Double] = [30, 35, 100,
300, 350, 1000]
let n = bValues.count
let xValues = [Double](unsafeUninitializedCapacity: n) {
buffer, count in
bValues.withUnsafeMutableBufferPointer { bPtr in
let B = DenseMatrix_Double(rowCount: 3,
columnCount: 2,
columnStride: 3,
attributes: SparseAttributes_t(),
data: bPtr.baseAddress!)
let X = DenseMatrix_Double(rowCount: 3,
columnCount: 2,
columnStride: 3,
attributes: SparseAttributes_t(),
data: buffer.baseAddress!)
SparseSolve(SparseLSMR(),
A, B, X)
count = n
}
}On return, xValues contains the values [1.0, 2.0, 3.0, 10.0, 20.0, 30.0].