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SparseSolve(_:_:_:)

Solves the system AX = B using the supplied single-precision factorization of A.

Declaration

func SparseSolve(_ Factored: SparseOpaqueFactorization_Float, _ B: DenseMatrix_Float, _ X: DenseMatrix_Float)

Parameters

  • Factored:

    The factorization of A.

  • B:

    The right-hand-side, B.

  • X:

    The matrix for returning solutions.

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 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: [Float] =       [10, 20, 5, 50]

let A = SparseConvertFromCoordinate(3, 3,
                                    4, 1,
                                    SparseAttributes_t(),
                                    rowIndices, columnIndices,
                                    aValues)

/// Factorize _A_.
let factorization = SparseFactor(SparseFactorizationQR, A)

defer {
    SparseCleanup(A)
    SparseCleanup(factorization)
}

/// Create the right-hand-side matrix, _b_.
var bValues: [Float] = [30, 35, 100,
                        300, 350, 1000]
let n = bValues.count

/// Solve the system.
let xValues = [Float](unsafeUninitializedCapacity: n) {
    buffer, count in
    bValues.withUnsafeMutableBufferPointer { bPtr in
        let B = DenseMatrix_Float(rowCount: 3,
                                  columnCount: 2,
                                  columnStride: 3,
                                  attributes: SparseAttributes_t(),
                                  data: bPtr.baseAddress!)
        
        let X = DenseMatrix_Float(rowCount: 3,
                                  columnCount: 2,
                                  columnStride: 3,
                                  attributes: SparseAttributes_t(),
                                  data: buffer.baseAddress!)
        
        SparseSolve(factorization, B, X)
        
        count = n
    }
}

On return, xValues contains the values [1.0, 2.0, 3.0, 10.0, 20.0, 30.0].

If the factorization is A = QR, the function returns the solution of minimum norm ‖ x ‖₂ for underdetermined systems.

If the factorization is A = QR, the function returns the least squares solution minₓ ‖ AX - B ‖₂ for overdetermined systems.

If the factorization is SparseFactorizationCholeskyAtA, the factorization is of AᵀA, and the solution that returns is for the system AᵀAX = B.

See Also

Out-of-place direct solving functions