diff --git a/unsupported/Eigen/src/IterativeSolvers/Scaling.h b/unsupported/Eigen/src/IterativeSolvers/Scaling.h
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+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Desire NUENTSA WAKAM .
+
+#ifndef EIGEN_SCALING_H
+#define EIGEN_SCALING_H
+/**
+ * \ingroup IterativeSolvers_Module
+ * \brief iterative scaling algorithm to equilibrate rows and column norms in matrices
+ *
+ * This class can be used as a preprocessing tool to accelerate the convergence of iterative methods
+ *
+ * This feature is useful to limit the pivoting amount during LU/ILU factorization
+ * The scaling strategy as presented here preserves the symmetry of the problem
+ * NOTE It is assumed that the matrix does not have empty row or column,
+ *
+ * Example with key steps
+ * \code
+ * VectorXd x(n), b(n);
+ * SparseMatrix A;
+ * // fill A and b;
+ * Scaling > scal;
+ * // Compute the left and right scaling vectors. The matrix is equilibrated at output
+ * scal.computeRef(A);
+ * // Scale the right hand side
+ * b = scal.LeftScaling().cwiseProduct(b);
+ * // Now, solve the equilibrated linear system with any available solver
+ *
+ * // Scale back the computed solution
+ * x = scal.RightScaling().cwiseProduct(x);
+ * \endcode
+ *
+ * \tparam _MatrixType the type of the matrix. It should be a real square sparsematrix
+ *
+ * References : D. Ruiz and B. Ucar, A Symmetry Preserving Algorithm for Matrix Scaling, INRIA Research report RR-7552
+ *
+ * \sa \ref IncompleteLUT
+ */
+using std::abs;
+using namespace Eigen;
+template
+class Scaling
+{
+ public:
+ typedef _MatrixType MatrixType;
+ typedef typename MatrixType::Scalar Scalar;
+ typedef typename MatrixType::Index Index;
+
+ public:
+ Scaling() { init(); }
+
+ Scaling(const MatrixType& matrix)
+ {
+ init();
+ compute(matrix);
+ }
+
+ ~Scaling() { }
+
+ /**
+ * Compute the left and right diagonal matrices to scale the input matrix @p mat
+ *
+ * FIXME This algorithm will be modified such that the diagonal elements are permuted on the diagonal.
+ *
+ * \sa LeftScaling() RightScaling()
+ */
+ void compute (const MatrixType& mat)
+ {
+ int m = mat.rows();
+ int n = mat.cols();
+ assert((m>0 && m == n) && "Please give a non - empty matrix");
+ m_left.resize(m);
+ m_right.resize(n);
+ m_left.setOnes();
+ m_right.setOnes();
+ m_matrix = mat;
+ VectorXd Dr, Dc, DrRes, DcRes; // Temporary Left and right scaling vectors
+ Dr.resize(m); Dc.resize(n);
+ DrRes.resize(m); DcRes.resize(n);
+ double EpsRow = 1.0, EpsCol = 1.0;
+ int its = 0;
+ do
+ { // Iterate until the infinite norm of each row and column is approximately 1
+ // Get the maximum value in each row and column
+ Dr.setZero(); Dc.setZero();
+ for (int k=0; km_tol || EpsCol > m_tol) && (its < m_maxits) );
+ m_isInitialized = true;
+ }
+ /** Compute the left and right vectors to scale the vectors
+ * the input matrix is scaled with the computed vectors at output
+ *
+ * \sa compute()
+ */
+ void computeRef (MatrixType& mat)
+ {
+ compute (mat);
+ mat = m_matrix;
+ }
+ /** Get the vector to scale the rows of the matrix
+ */
+ VectorXd& LeftScaling()
+ {
+ return m_left;
+ }
+
+ /** Get the vector to scale the columns of the matrix
+ */
+ VectorXd& RightScaling()
+ {
+ return m_right;
+ }
+
+ /** Set the tolerance for the convergence of the iterative scaling algorithm
+ */
+ void setTolerance(double tol)
+ {
+ m_tol = tol;
+ }
+
+ protected:
+
+ void init()
+ {
+ m_tol = 1e-10;
+ m_maxits = 5;
+ m_isInitialized = false;
+ }
+
+ MatrixType m_matrix;
+ mutable ComputationInfo m_info;
+ bool m_isInitialized;
+ VectorXd m_left; // Left scaling vector
+ VectorXd m_right; // m_right scaling vector
+ double m_tol;
+ int m_maxits; // Maximum number of iterations allowed
+};
+
+#endif
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