Function precondition

Applications/HHL_Algorithm/preconditionByOrigin.cpp:316–358

void precondition(SparseQMatrix<double>& M, SparseQMatrix<double>& A, vector<double>& b, 
	              int KindLinPrec, int SparseIndex, double DySPepsilon) {
	auto dim = M.size;
	vector<vector<double>> diagD;
	vector<double> d1;
	//vector<double> d2;
	SparseQMatrix<double> Ms;

	switch (KindLinPrec) {
	case NO_PRE:
		A = M;
		break;
	case DIAGNOAL_SCALING:
		DiagonalScalingNew(M, diagD);
		cout << "DiagonalScaling is over" << endl;
		d1 = diagD[0];
		dright = diagD[1];
		A = M;
		for (int i = 0; i < dim; i++) {
			b[i] = b[i] * d1[i];
		}
		break;
	case STATIC_SPAI:
		Ms = StaticSparseApproximateInverse(M, SparseIndex);
		cout << "Static Sparse Approximate Inverse is over" << endl;
		A = MprodcutA(Ms, M, SparseIndex);
		b = MproductbDen(Ms, b);
		break;
	case DYNAMIC_SPAI:
		A = DynamicSparseApproximateInverse(M, Ms, DySPepsilon, SparseIndex);
		cout << "Dynamic Sparse Approximate Inverse is over" << endl;
		b = MproductbDen(Ms, b);
		break;
	case POWER_SAI_LEFT:
		A = PowerSparseApproximateInverseLeft(M, Ms, DySPepsilon, SparseIndex);
		cout << "Power Sparse Approximate Inverse is over" << endl;
		b = MproductbDen(Ms, b);
		break;
	case JACOBI_P:
		A = JacobiPrecondition(M, b);
		break;
	}
}

std::pair< Eigen::MatrixXd, Eigen::VectorXd > DynamicSparseApproximateInverse(Eigen::MatrixXd& Ae, Eigen::VectorXd& b, double epsilon, int SparseIndex, Eigen::MatrixXd& M) {
	//sparI = read_sparsity();