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diff --git a/runtimes/nn/depend/external/eigen/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h b/runtimes/nn/depend/external/eigen/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
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-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// 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_CONJUGATE_GRADIENT_H
-#define EIGEN_CONJUGATE_GRADIENT_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \internal Low-level conjugate gradient algorithm
-  * \param mat The matrix A
-  * \param rhs The right hand side vector b
-  * \param x On input and initial solution, on output the computed solution.
-  * \param precond A preconditioner being able to efficiently solve for an
-  *                approximation of Ax=b (regardless of b)
-  * \param iters On input the max number of iteration, on output the number of performed iterations.
-  * \param tol_error On input the tolerance error, on output an estimation of the relative error.
-  */
-template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
-EIGEN_DONT_INLINE
-void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
-                        const Preconditioner& precond, Index& iters,
-                        typename Dest::RealScalar& tol_error)
-{
-  using std::sqrt;
-  using std::abs;
-  typedef typename Dest::RealScalar RealScalar;
-  typedef typename Dest::Scalar Scalar;
-  typedef Matrix<Scalar,Dynamic,1> VectorType;
-  
-  RealScalar tol = tol_error;
-  Index maxIters = iters;
-  
-  Index n = mat.cols();
-
-  VectorType residual = rhs - mat * x; //initial residual
-
-  RealScalar rhsNorm2 = rhs.squaredNorm();
-  if(rhsNorm2 == 0) 
-  {
-    x.setZero();
-    iters = 0;
-    tol_error = 0;
-    return;
-  }
-  RealScalar threshold = tol*tol*rhsNorm2;
-  RealScalar residualNorm2 = residual.squaredNorm();
-  if (residualNorm2 < threshold)
-  {
-    iters = 0;
-    tol_error = sqrt(residualNorm2 / rhsNorm2);
-    return;
-  }
-  
-  VectorType p(n);
-  p = precond.solve(residual);      // initial search direction
-
-  VectorType z(n), tmp(n);
-  RealScalar absNew = numext::real(residual.dot(p));  // the square of the absolute value of r scaled by invM
-  Index i = 0;
-  while(i < maxIters)
-  {
-    tmp.noalias() = mat * p;                    // the bottleneck of the algorithm
-
-    Scalar alpha = absNew / p.dot(tmp);         // the amount we travel on dir
-    x += alpha * p;                             // update solution
-    residual -= alpha * tmp;                    // update residual
-    
-    residualNorm2 = residual.squaredNorm();
-    if(residualNorm2 < threshold)
-      break;
-    
-    z = precond.solve(residual);                // approximately solve for "A z = residual"
-
-    RealScalar absOld = absNew;
-    absNew = numext::real(residual.dot(z));     // update the absolute value of r
-    RealScalar beta = absNew / absOld;          // calculate the Gram-Schmidt value used to create the new search direction
-    p = z + beta * p;                           // update search direction
-    i++;
-  }
-  tol_error = sqrt(residualNorm2 / rhsNorm2);
-  iters = i;
-}
-
-}
-
-template< typename _MatrixType, int _UpLo=Lower,
-          typename _Preconditioner = DiagonalPreconditioner<typename _MatrixType::Scalar> >
-class ConjugateGradient;
-
-namespace internal {
-
-template< typename _MatrixType, int _UpLo, typename _Preconditioner>
-struct traits<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> >
-{
-  typedef _MatrixType MatrixType;
-  typedef _Preconditioner Preconditioner;
-};
-
-}
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief A conjugate gradient solver for sparse (or dense) self-adjoint problems
-  *
-  * This class allows to solve for A.x = b linear problems using an iterative conjugate gradient algorithm.
-  * The matrix A must be selfadjoint. The matrix A and the vectors x and b can be either dense or sparse.
-  *
-  * \tparam _MatrixType the type of the matrix A, can be a dense or a sparse matrix.
-  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower,
-  *               \c Upper, or \c Lower|Upper in which the full matrix entries will be considered.
-  *               Default is \c Lower, best performance is \c Lower|Upper.
-  * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
-  *
-  * \implsparsesolverconcept
-  *
-  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
-  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
-  * and NumTraits<Scalar>::epsilon() for the tolerance.
-  * 
-  * The tolerance corresponds to the relative residual error: |Ax-b|/|b|
-  * 
-  * \b Performance: Even though the default value of \c _UpLo is \c Lower, significantly higher performance is
-  * achieved when using a complete matrix and \b Lower|Upper as the \a _UpLo template parameter. Moreover, in this
-  * case multi-threading can be exploited if the user code is compiled with OpenMP enabled.
-  * See \ref TopicMultiThreading for details.
-  * 
-  * This class can be used as the direct solver classes. Here is a typical usage example:
-    \code
-    int n = 10000;
-    VectorXd x(n), b(n);
-    SparseMatrix<double> A(n,n);
-    // fill A and b
-    ConjugateGradient<SparseMatrix<double>, Lower|Upper> cg;
-    cg.compute(A);
-    x = cg.solve(b);
-    std::cout << "#iterations:     " << cg.iterations() << std::endl;
-    std::cout << "estimated error: " << cg.error()      << std::endl;
-    // update b, and solve again
-    x = cg.solve(b);
-    \endcode
-  * 
-  * By default the iterations start with x=0 as an initial guess of the solution.
-  * One can control the start using the solveWithGuess() method.
-  * 
-  * ConjugateGradient can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
-  *
-  * \sa class LeastSquaresConjugateGradient, class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
-  */
-template< typename _MatrixType, int _UpLo, typename _Preconditioner>
-class ConjugateGradient : public IterativeSolverBase<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> >
-{
-  typedef IterativeSolverBase<ConjugateGradient> Base;
-  using Base::matrix;
-  using Base::m_error;
-  using Base::m_iterations;
-  using Base::m_info;
-  using Base::m_isInitialized;
-public:
-  typedef _MatrixType MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef _Preconditioner Preconditioner;
-
-  enum {
-    UpLo = _UpLo
-  };
-
-public:
-
-  /** Default constructor. */
-  ConjugateGradient() : Base() {}
-
-  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
-    * 
-    * This constructor is a shortcut for the default constructor followed
-    * by a call to compute().
-    * 
-    * \warning this class stores a reference to the matrix A as well as some
-    * precomputed values that depend on it. Therefore, if \a A is changed
-    * this class becomes invalid. Call compute() to update it with the new
-    * matrix A, or modify a copy of A.
-    */
-  template<typename MatrixDerived>
-  explicit ConjugateGradient(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}
-
-  ~ConjugateGradient() {}
-
-  /** \internal */
-  template<typename Rhs,typename Dest>
-  void _solve_with_guess_impl(const Rhs& b, Dest& x) const
-  {
-    typedef typename Base::MatrixWrapper MatrixWrapper;
-    typedef typename Base::ActualMatrixType ActualMatrixType;
-    enum {
-      TransposeInput  =   (!MatrixWrapper::MatrixFree)
-                      &&  (UpLo==(Lower|Upper))
-                      &&  (!MatrixType::IsRowMajor)
-                      &&  (!NumTraits<Scalar>::IsComplex)
-    };
-    typedef typename internal::conditional<TransposeInput,Transpose<const ActualMatrixType>, ActualMatrixType const&>::type RowMajorWrapper;
-    EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(MatrixWrapper::MatrixFree,UpLo==(Lower|Upper)),MATRIX_FREE_CONJUGATE_GRADIENT_IS_COMPATIBLE_WITH_UPPER_UNION_LOWER_MODE_ONLY);
-    typedef typename internal::conditional<UpLo==(Lower|Upper),
-                                           RowMajorWrapper,
-                                           typename MatrixWrapper::template ConstSelfAdjointViewReturnType<UpLo>::Type
-                                          >::type SelfAdjointWrapper;
-    m_iterations = Base::maxIterations();
-    m_error = Base::m_tolerance;
-
-    for(Index j=0; j<b.cols(); ++j)
-    {
-      m_iterations = Base::maxIterations();
-      m_error = Base::m_tolerance;
-
-      typename Dest::ColXpr xj(x,j);
-      RowMajorWrapper row_mat(matrix());
-      internal::conjugate_gradient(SelfAdjointWrapper(row_mat), b.col(j), xj, Base::m_preconditioner, m_iterations, m_error);
-    }
-
-    m_isInitialized = true;
-    m_info = m_error <= Base::m_tolerance ? Success : NoConvergence;
-  }
-  
-  /** \internal */
-  using Base::_solve_impl;
-  template<typename Rhs,typename Dest>
-  void _solve_impl(const MatrixBase<Rhs>& b, Dest& x) const
-  {
-    x.setZero();
-    _solve_with_guess_impl(b.derived(),x);
-  }
-
-protected:
-
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CONJUGATE_GRADIENT_H