quick reimplementation of SVD from the numeral recipes book:

this is still not Eigen style code but at least it works for
n>m and it is more accurate than the JAMA based version. (I needed
it now, this is why I did that)

1 files changed, 1 insertions, 1 deletions

diff --git a/test/svd.cpp b/test/svd.cpp
index 2eb7881a0..93e5ea017 100644
--- a/test/svd.cpp
+++ b/test/svd.cpp
@@ -50,7 +50,7 @@ template<typename MatrixType> void svd(const MatrixType& m)
     MatrixType sigma = MatrixType::Zero(rows,cols);
     MatrixType matU  = MatrixType::Zero(rows,rows);
     sigma.block(0,0,cols,cols) = svd.singularValues().asDiagonal();
-    matU.block(0,0,rows,cols) = svd.matrixU();
+    matU = svd.matrixU();
     VERIFY_IS_APPROX(a, matU * sigma * svd.matrixV().transpose());
   }