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diff --git a/eigen/test/eigen2/eigen2_cholesky.cpp b/eigen/test/eigen2/eigen2_cholesky.cpp
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+++ b/eigen/test/eigen2/eigen2_cholesky.cpp
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+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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/.
+
+#define EIGEN_NO_ASSERTION_CHECKING
+#include "main.h"
+#include <Eigen/Cholesky>
+#include <Eigen/LU>
+
+#ifdef HAS_GSL
+#include "gsl_helper.h"
+#endif
+
+template<typename MatrixType> void cholesky(const MatrixType& m)
+{
+  /* this test covers the following files:
+     LLT.h LDLT.h
+  */
+  int rows = m.rows();
+  int cols = m.cols();
+
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;
+  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
+
+  MatrixType a0 = MatrixType::Random(rows,cols);
+  VectorType vecB = VectorType::Random(rows), vecX(rows);
+  MatrixType matB = MatrixType::Random(rows,cols), matX(rows,cols);
+  SquareMatrixType symm =  a0 * a0.adjoint();
+  // let's make sure the matrix is not singular or near singular
+  MatrixType a1 = MatrixType::Random(rows,cols);
+  symm += a1 * a1.adjoint();
+
+  #ifdef HAS_GSL
+  if (ei_is_same_type<RealScalar,double>::ret)
+  {
+    typedef GslTraits<Scalar> Gsl;
+    typename Gsl::Matrix gMatA=0, gSymm=0;
+    typename Gsl::Vector gVecB=0, gVecX=0;
+    convert<MatrixType>(symm, gSymm);
+    convert<MatrixType>(symm, gMatA);
+    convert<VectorType>(vecB, gVecB);
+    convert<VectorType>(vecB, gVecX);
+    Gsl::cholesky(gMatA);
+    Gsl::cholesky_solve(gMatA, gVecB, gVecX);
+    VectorType vecX(rows), _vecX, _vecB;
+    convert(gVecX, _vecX);
+    symm.llt().solve(vecB, &vecX);
+    Gsl::prod(gSymm, gVecX, gVecB);
+    convert(gVecB, _vecB);
+    // test gsl itself !
+    VERIFY_IS_APPROX(vecB, _vecB);
+    VERIFY_IS_APPROX(vecX, _vecX);
+
+    Gsl::free(gMatA);
+    Gsl::free(gSymm);
+    Gsl::free(gVecB);
+    Gsl::free(gVecX);
+  }
+  #endif
+
+  {
+    LDLT<SquareMatrixType> ldlt(symm);
+    VERIFY(ldlt.isPositiveDefinite());
+    // in eigen3, LDLT is pivoting
+    //VERIFY_IS_APPROX(symm, ldlt.matrixL() * ldlt.vectorD().asDiagonal() * ldlt.matrixL().adjoint());
+    ldlt.solve(vecB, &vecX);
+    VERIFY_IS_APPROX(symm * vecX, vecB);
+    ldlt.solve(matB, &matX);
+    VERIFY_IS_APPROX(symm * matX, matB);
+  }
+
+  {
+    LLT<SquareMatrixType> chol(symm);
+    VERIFY(chol.isPositiveDefinite());
+    VERIFY_IS_APPROX(symm, chol.matrixL() * chol.matrixL().adjoint());
+    chol.solve(vecB, &vecX);
+    VERIFY_IS_APPROX(symm * vecX, vecB);
+    chol.solve(matB, &matX);
+    VERIFY_IS_APPROX(symm * matX, matB);
+  }
+
+#if 0 // cholesky is not rank-revealing anyway
+  // test isPositiveDefinite on non definite matrix
+  if (rows>4)
+  {
+    SquareMatrixType symm =  a0.block(0,0,rows,cols-4) * a0.block(0,0,rows,cols-4).adjoint();
+    LLT<SquareMatrixType> chol(symm);
+    VERIFY(!chol.isPositiveDefinite());
+    LDLT<SquareMatrixType> cholnosqrt(symm);
+    VERIFY(!cholnosqrt.isPositiveDefinite());
+  }
+#endif
+}
+
+void test_eigen2_cholesky()
+{
+  for(int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST_1( cholesky(Matrix<double,1,1>()) );
+    CALL_SUBTEST_2( cholesky(Matrix2d()) );
+    CALL_SUBTEST_3( cholesky(Matrix3f()) );
+    CALL_SUBTEST_4( cholesky(Matrix4d()) );
+    CALL_SUBTEST_5( cholesky(MatrixXcd(7,7)) );
+    CALL_SUBTEST_6( cholesky(MatrixXf(17,17)) );
+    CALL_SUBTEST_7( cholesky(MatrixXd(33,33)) );
+  }
+}