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diff --git a/eigen/unsupported/Eigen/src/Polynomials/Companion.h b/eigen/unsupported/Eigen/src/Polynomials/Companion.h
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--- a/eigen/unsupported/Eigen/src/Polynomials/Companion.h
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@@ -1,276 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Manuel Yguel <manuel.yguel@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPANION_H
-#define EIGEN_COMPANION_H
-
-// This file requires the user to include
-// * Eigen/Core
-// * Eigen/src/PolynomialSolver.h
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-template <typename T>
-T radix(){ return 2; }
-
-template <typename T>
-T radix2(){ return radix<T>()*radix<T>(); }
-
-template<int Size>
-struct decrement_if_fixed_size
-{
- enum {
- ret = (Size == Dynamic) ? Dynamic : Size-1 };
-};
-
-#endif
-
-template< typename _Scalar, int _Deg >
-class companion
-{
- public:
- EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Deg==Dynamic ? Dynamic : _Deg)
-
- enum {
- Deg = _Deg,
- Deg_1=decrement_if_fixed_size<Deg>::ret
- };
-
- typedef _Scalar Scalar;
- typedef typename NumTraits<Scalar>::Real RealScalar;
- typedef Matrix<Scalar, Deg, 1> RightColumn;
- //typedef DiagonalMatrix< Scalar, Deg_1, Deg_1 > BottomLeftDiagonal;
- typedef Matrix<Scalar, Deg_1, 1> BottomLeftDiagonal;
-
- typedef Matrix<Scalar, Deg, Deg> DenseCompanionMatrixType;
- typedef Matrix< Scalar, _Deg, Deg_1 > LeftBlock;
- typedef Matrix< Scalar, Deg_1, Deg_1 > BottomLeftBlock;
- typedef Matrix< Scalar, 1, Deg_1 > LeftBlockFirstRow;
-
- typedef DenseIndex Index;
-
- public:
- EIGEN_STRONG_INLINE const _Scalar operator()(Index row, Index col ) const
- {
- if( m_bl_diag.rows() > col )
- {
- if( 0 < row ){ return m_bl_diag[col]; }
- else{ return 0; }
- }
- else{ return m_monic[row]; }
- }
-
- public:
- template<typename VectorType>
- void setPolynomial( const VectorType& poly )
- {
- const Index deg = poly.size()-1;
- m_monic = -1/poly[deg] * poly.head(deg);
- //m_bl_diag.setIdentity( deg-1 );
- m_bl_diag.setOnes(deg-1);
- }
-
- template<typename VectorType>
- companion( const VectorType& poly ){
- setPolynomial( poly ); }
-
- public:
- DenseCompanionMatrixType denseMatrix() const
- {
- const Index deg = m_monic.size();
- const Index deg_1 = deg-1;
- DenseCompanionMatrixType companion(deg,deg);
- companion <<
- ( LeftBlock(deg,deg_1)
- << LeftBlockFirstRow::Zero(1,deg_1),
- BottomLeftBlock::Identity(deg-1,deg-1)*m_bl_diag.asDiagonal() ).finished()
- , m_monic;
- return companion;
- }
-
-
-
- protected:
- /** Helper function for the balancing algorithm.
- * \returns true if the row and the column, having colNorm and rowNorm
- * as norms, are balanced, false otherwise.
- * colB and rowB are repectively the multipliers for
- * the column and the row in order to balance them.
- * */
- bool balanced( Scalar colNorm, Scalar rowNorm,
- bool& isBalanced, Scalar& colB, Scalar& rowB );
-
- /** Helper function for the balancing algorithm.
- * \returns true if the row and the column, having colNorm and rowNorm
- * as norms, are balanced, false otherwise.
- * colB and rowB are repectively the multipliers for
- * the column and the row in order to balance them.
- * */
- bool balancedR( Scalar colNorm, Scalar rowNorm,
- bool& isBalanced, Scalar& colB, Scalar& rowB );
-
- public:
- /**
- * Balancing algorithm from B. N. PARLETT and C. REINSCH (1969)
- * "Balancing a matrix for calculation of eigenvalues and eigenvectors"
- * adapted to the case of companion matrices.
- * A matrix with non zero row and non zero column is balanced
- * for a certain norm if the i-th row and the i-th column
- * have same norm for all i.
- */
- void balance();
-
- protected:
- RightColumn m_monic;
- BottomLeftDiagonal m_bl_diag;
-};
-
-
-
-template< typename _Scalar, int _Deg >
-inline
-bool companion<_Scalar,_Deg>::balanced( Scalar colNorm, Scalar rowNorm,
- bool& isBalanced, Scalar& colB, Scalar& rowB )
-{
- if( Scalar(0) == colNorm || Scalar(0) == rowNorm ){ return true; }
- else
- {
- //To find the balancing coefficients, if the radix is 2,
- //one finds \f$ \sigma \f$ such that
- // \f$ 2^{2\sigma-1} < rowNorm / colNorm \le 2^{2\sigma+1} \f$
- // then the balancing coefficient for the row is \f$ 1/2^{\sigma} \f$
- // and the balancing coefficient for the column is \f$ 2^{\sigma} \f$
- rowB = rowNorm / radix<Scalar>();
- colB = Scalar(1);
- const Scalar s = colNorm + rowNorm;
-
- while (colNorm < rowB)
- {
- colB *= radix<Scalar>();
- colNorm *= radix2<Scalar>();
- }
-
- rowB = rowNorm * radix<Scalar>();
-
- while (colNorm >= rowB)
- {
- colB /= radix<Scalar>();
- colNorm /= radix2<Scalar>();
- }
-
- //This line is used to avoid insubstantial balancing
- if ((rowNorm + colNorm) < Scalar(0.95) * s * colB)
- {
- isBalanced = false;
- rowB = Scalar(1) / colB;
- return false;
- }
- else{
- return true; }
- }
-}
-
-template< typename _Scalar, int _Deg >
-inline
-bool companion<_Scalar,_Deg>::balancedR( Scalar colNorm, Scalar rowNorm,
- bool& isBalanced, Scalar& colB, Scalar& rowB )
-{
- if( Scalar(0) == colNorm || Scalar(0) == rowNorm ){ return true; }
- else
- {
- /**
- * Set the norm of the column and the row to the geometric mean
- * of the row and column norm
- */
- const _Scalar q = colNorm/rowNorm;
- if( !isApprox( q, _Scalar(1) ) )
- {
- rowB = sqrt( colNorm/rowNorm );
- colB = Scalar(1)/rowB;
-
- isBalanced = false;
- return false;
- }
- else{
- return true; }
- }
-}
-
-
-template< typename _Scalar, int _Deg >
-void companion<_Scalar,_Deg>::balance()
-{
- using std::abs;
- EIGEN_STATIC_ASSERT( Deg == Dynamic || 1 < Deg, YOU_MADE_A_PROGRAMMING_MISTAKE );
- const Index deg = m_monic.size();
- const Index deg_1 = deg-1;
-
- bool hasConverged=false;
- while( !hasConverged )
- {
- hasConverged = true;
- Scalar colNorm,rowNorm;
- Scalar colB,rowB;
-
- //First row, first column excluding the diagonal
- //==============================================
- colNorm = abs(m_bl_diag[0]);
- rowNorm = abs(m_monic[0]);
-
- //Compute balancing of the row and the column
- if( !balanced( colNorm, rowNorm, hasConverged, colB, rowB ) )
- {
- m_bl_diag[0] *= colB;
- m_monic[0] *= rowB;
- }
-
- //Middle rows and columns excluding the diagonal
- //==============================================
- for( Index i=1; i<deg_1; ++i )
- {
- // column norm, excluding the diagonal
- colNorm = abs(m_bl_diag[i]);
-
- // row norm, excluding the diagonal
- rowNorm = abs(m_bl_diag[i-1]) + abs(m_monic[i]);
-
- //Compute balancing of the row and the column
- if( !balanced( colNorm, rowNorm, hasConverged, colB, rowB ) )
- {
- m_bl_diag[i] *= colB;
- m_bl_diag[i-1] *= rowB;
- m_monic[i] *= rowB;
- }
- }
-
- //Last row, last column excluding the diagonal
- //============================================
- const Index ebl = m_bl_diag.size()-1;
- VectorBlock<RightColumn,Deg_1> headMonic( m_monic, 0, deg_1 );
- colNorm = headMonic.array().abs().sum();
- rowNorm = abs( m_bl_diag[ebl] );
-
- //Compute balancing of the row and the column
- if( !balanced( colNorm, rowNorm, hasConverged, colB, rowB ) )
- {
- headMonic *= colB;
- m_bl_diag[ebl] *= rowB;
- }
- }
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPANION_H
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