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public static qrDecomposition ( Matrix M, Matrix q, Matrix r, bool pivot ) : List |
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| M | Matrix | |
| q | Matrix | |
| r | Matrix | |
| pivot | bool | |
| Результат | List |
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//! QR Solve
/*! This implementation is based on MINPACK
* (<http://www.netlib.org/minpack>,
* <http://www.netlib.org/cephes/linalg.tgz>)
*
* Given an m by n matrix A, an n by n diagonal matrix d,
* and an m-vector b, the problem is to determine an x which
* solves the system
*
* A*x = b , d*x = 0 ,
*
* in the least squares sense.
*
* d is an input array of length n which must contain the
* diagonal elements of the matrix d.
*
* See lmdiff.cpp for further details.
*/
public static Vector qrSolve(Matrix a, Vector b, bool pivot = true, Vector d = null)
{
int m = a.rows();
int n = a.columns();
if (d == null)
{
d = new Vector();
}
Utils.QL_REQUIRE(b.Count == m, () => "dimensions of A and b don't match");
Utils.QL_REQUIRE(d.Count == n || d.empty(), () => "dimensions of A and d don't match");
Matrix q = new Matrix(m, n), r = new Matrix(n, n);
List <int> lipvt = MatrixUtilities.qrDecomposition(a, ref q, ref r, pivot);
List <int> ipvt = new List <int>(n);
ipvt = lipvt;
//std::copy(lipvt.begin(), lipvt.end(), ipvt.get());
Matrix aT = Matrix.transpose(a);
Matrix rT = Matrix.transpose(r);
Vector sdiag = new Vector(n);
Vector wa = new Vector(n);
Vector ld = new Vector(n, 0.0);
if (!d.empty())
{
ld = d;
//std::copy(d.begin(), d.end(), ld.begin());
}
Vector x = new Vector(n);
Vector qtb = Matrix.transpose(q) * b;
MINPACK.qrsolv(n, rT, n, ipvt, ld, qtb, x, sdiag, wa);
return(x);
}
