public LeastSquaresSolver(int Row_number, int Column_number)
{
m_A = MathNetNumLin.Matrix <double> .Build.Dense(Row_number, Column_number);
m_xInitial = MathNetNumLin.Vector <double> .Build.Dense(Column_number, 1);
m_xLowerBound = MathNetNumLin.Vector <double> .Build.Dense(Column_number, 1);
m_xUpperBound = MathNetNumLin.Vector <double> .Build.Dense(Column_number, 1);
m_b = MathNetNumLin.Vector <double> .Build.Dense(Row_number, 1);
m_results = MathNetNumLin.Vector <double> .Build.Dense(Column_number, 1);
RowNumber = m_A.RowCount;
ColumnNumber = m_A.ColumnCount;
}
public MathNetNumLin.Matrix <Complex> LeastSquareEstimationInequalityConstrained(MathNetNumLin.Matrix <Complex> b, MathNetNumLin.Matrix <Complex> A)
{
int Row_number = A.RowCount;
int Column_number = A.ColumnCount;
MathNetNumLin.Matrix <double> A_realpart = MathNetNumLin.Matrix <double> .Build.Dense(Row_number, Column_number);
MathNetNumLin.Matrix <double> A_imagpart = MathNetNumLin.Matrix <double> .Build.Dense(Row_number, Column_number);
MathNetNumLin.Vector <double> b_realpart = MathNetNumLin.Vector <double> .Build.Dense(Row_number);
MathNetNumLin.Vector <double> b_imagpart = MathNetNumLin.Vector <double> .Build.Dense(Row_number);
for (int idx0 = 0; idx0 < Row_number; idx0++)
{
for (int idx1 = 0; idx1 < Column_number; idx1++)
{
A_realpart[idx0, idx1] = A[idx0, idx1].Real;
A_imagpart[idx0, idx1] = A[idx0, idx1].Imaginary;
}
b_realpart[idx0] = b[idx0, 0].Real;
b_imagpart[idx0] = b[idx0, 0].Imaginary;
}
MathNetNumLin.Vector <double> xInitial_realpart = MathNetNumLin.Vector <double> .Build.Dense(Column_number);
MathNetNumLin.Vector <double> xInitial_imagpart = MathNetNumLin.Vector <double> .Build.Dense(Column_number);
MathNetNumLin.Vector <double> xLowerBound_realpart = MathNetNumLin.Vector <double> .Build.Dense(Column_number);
MathNetNumLin.Vector <double> xLowerBound_imagpart = MathNetNumLin.Vector <double> .Build.Dense(Column_number);
MathNetNumLin.Vector <double> xUpperBound_realpart = MathNetNumLin.Vector <double> .Build.Dense(Column_number);
MathNetNumLin.Vector <double> xUpperBound_imagpart = MathNetNumLin.Vector <double> .Build.Dense(Column_number);
for (int idx2 = 0; idx2 < Column_number; idx2++)
{
xInitial_realpart[idx2] = 1;
xInitial_imagpart[idx2] = 0;
xLowerBound_realpart[idx2] = 0.9452;
xLowerBound_imagpart[idx2] = -0.1005;
xUpperBound_realpart[idx2] = 1.0526;
xUpperBound_imagpart[idx2] = 0.1005;
}
MathNetNumLin.Matrix <Complex> Beta = MathNetNumLin.Matrix <Complex> .Build.Random(Column_number, 1);
MathNetNumLin.Vector <double> RealBeta = MathNetNumLin.Vector <double> .Build.Random(Column_number);
LeastSquaresSolver RealPartSolver = new LeastSquaresSolver(Row_number, Column_number);
RealPartSolver.A = A_realpart;
RealPartSolver.b = b_realpart;
RealPartSolver.xInitial = xInitial_realpart;
RealPartSolver.xLowerBound = xLowerBound_realpart;
RealPartSolver.xUpperBound = xUpperBound_realpart;
RealPartSolver.LeastSqauresInequalityConstrainedEstimation();
for (int idx3 = 0; idx3 < RealPartSolver.Results.Count; idx3++)
{
RealBeta[idx3] = RealPartSolver.Results[idx3];
}
LeastSquaresSolver ImagPartSolver = new LeastSquaresSolver(Row_number, Column_number);
ImagPartSolver.A = A_imagpart;
ImagPartSolver.b = b_imagpart;
ImagPartSolver.xInitial = xInitial_imagpart;
ImagPartSolver.xLowerBound = xLowerBound_imagpart;
ImagPartSolver.xUpperBound = xUpperBound_imagpart;
ImagPartSolver.LeastSqauresInequalityConstrainedEstimation();
for (int idx4 = 0; idx4 < RealPartSolver.Results.Count; idx4++)
{
Beta[idx4, 0] = new Complex(RealBeta[idx4], ImagPartSolver.Results[idx4]);
}
return(Beta);
}