diff --git a/Eigen/src/SVD/JacobiSVD.h b/Eigen/src/SVD/JacobiSVD.h
index f3b0ccce4..44880dcf4 100644
--- a/Eigen/src/SVD/JacobiSVD.h
+++ b/Eigen/src/SVD/JacobiSVD.h
@@ -239,6 +239,9 @@ struct ei_qr_preconditioner_impl<MatrixType, HouseholderQRPreconditioner, Precon
   * \a p is the greater dimension, meaning that it is still of the same order of complexity as the faster bidiagonalizing R-SVD algorithms.
   * In particular, like any R-SVD, it takes advantage of non-squareness in that its complexity is only linear in the greater dimension.
   *
+  * If the input matrix has inf or nan coefficients, the result of the computation is undefined, but the computation is guaranteed to
+  * terminate in finite (and reasonable) time.
+  * 
   * The possible values for QRPreconditioner are:
   * \li ColPivHouseholderQRPreconditioner is the default. In practice it's very safe. It uses column-pivoting QR.
   * \li FullPivHouseholderQRPreconditioner, is the safest and slowest. It uses full-pivoting QR.
diff --git a/test/jacobisvd.cpp b/test/jacobisvd.cpp
index 212f7f1d7..79a05b368 100644
--- a/test/jacobisvd.cpp
+++ b/test/jacobisvd.cpp
@@ -209,18 +209,30 @@ void jacobisvd_method()
   VERIFY_IS_APPROX(m.jacobiSvd(ComputeFullU|ComputeFullV).solve(m), m);
 }
 
+// work around stupid msvc error when constructing at compile time an expression that involves
+// a division by zero, even if the numeric type has floating point
+template<typename Scalar>
+EIGEN_DONT_INLINE Scalar zero() { return Scalar(0); }
+
 template<typename MatrixType>
 void jacobisvd_inf_nan()
 {
+  // all this function does is verify we don't iterate infinitely on nan/inf values
+
   JacobiSVD<MatrixType> svd;
   typedef typename MatrixType::Scalar Scalar;
-  Scalar some_inf = Scalar(1) / Scalar(0);
+  Scalar some_inf = Scalar(1) / zero<Scalar>();
+  VERIFY((some_inf - some_inf) != (some_inf - some_inf));
   svd.compute(MatrixType::Constant(10,10,some_inf), ComputeFullU | ComputeFullV);
-  Scalar some_nan = Scalar(0) / Scalar(0);
+
+  Scalar some_nan = zero<Scalar>() / zero<Scalar>();
+  VERIFY(some_nan != some_nan);
   svd.compute(MatrixType::Constant(10,10,some_nan), ComputeFullU | ComputeFullV);
+
   MatrixType m = MatrixType::Zero(10,10);
   m(ei_random<int>(0,9), ei_random<int>(0,9)) = some_inf;
   svd.compute(m, ComputeFullU | ComputeFullV);
+
   m = MatrixType::Zero(10,10);
   m(ei_random<int>(0,9), ei_random<int>(0,9)) = some_nan;
   svd.compute(m, ComputeFullU | ComputeFullV);