diff --git a/projects/Math/76/org/apache/commons/math/linear/SingularValueDecompositionImpl.java b/projects/Math/76/org/apache/commons/math/linear/SingularValueDecompositionImpl.java
index 1436881..e418c08 100644
--- a/projects/Math/76/org/apache/commons/math/linear/SingularValueDecompositionImpl.java
+++ b/projects/Math/76/org/apache/commons/math/linear/SingularValueDecompositionImpl.java
@@ -159,24 +159,27 @@ public RealMatrix getU()
             if (m >= n) {
                 // the tridiagonal matrix is Bt.B, where B is upper bidiagonal
                 final RealMatrix e =
-                    eigenDecomposition.getV().getSubMatrix(0, p - 1, 0, p - 1);
+                    eigenDecomposition.getV().getSubMatrix(0, n - 1, 0, p - 1);
                 final double[][] eData = e.getData();
                 final double[][] wData = new double[m][p];
                 double[] ei1 = eData[0];
-                for (int i = 0; i < p - 1; ++i) {
+                for (int i = 0; i < p; ++i) {
                     // compute W = B.E.S^(-1) where E is the eigenvectors matrix
                     final double mi = mainBidiagonal[i];
                     final double[] ei0 = ei1;
                     final double[] wi  = wData[i];
+                    if (i < n - 1) {
                         ei1 = eData[i + 1];
                         final double si = secondaryBidiagonal[i];
                         for (int j = 0; j < p; ++j) {
                             wi[j] = (mi * ei0[j] + si * ei1[j]) / singularValues[j];
                         }
-                }
+                    } else {
                         for (int j = 0; j < p; ++j) {
-                            wData[p - 1][j] = ei1[j] * mainBidiagonal[p - 1] / singularValues[j];
+                            wi[j] = mi * ei0[j] / singularValues[j];
                         }
+                    }
+                }
 
                 for (int i = p; i < m; ++i) {
                     wData[i] = new double[p];
@@ -245,23 +248,26 @@ public RealMatrix getV()
                 // the tridiagonal matrix is B.Bt, where B is lower bidiagonal
                 // compute W = Bt.E.S^(-1) where E is the eigenvectors matrix
                 final RealMatrix e =
-                    eigenDecomposition.getV().getSubMatrix(0, p - 1, 0, p - 1);
+                    eigenDecomposition.getV().getSubMatrix(0, m - 1, 0, p - 1);
                 final double[][] eData = e.getData();
                 final double[][] wData = new double[n][p];
                 double[] ei1 = eData[0];
-                for (int i = 0; i < p - 1; ++i) {
+                for (int i = 0; i < p; ++i) {
                     final double mi = mainBidiagonal[i];
                     final double[] ei0 = ei1;
                     final double[] wi  = wData[i];
+                    if (i < m - 1) {
                         ei1 = eData[i + 1];
                         final double si = secondaryBidiagonal[i];
                         for (int j = 0; j < p; ++j) {
                             wi[j] = (mi * ei0[j] + si * ei1[j]) / singularValues[j];
                         }
-                }
+                    } else {
                         for (int j = 0; j < p; ++j) {
-                            wData[p - 1][j] = ei1[j] * mainBidiagonal[p - 1] / singularValues[j];
+                            wi[j] = mi * ei0[j] / singularValues[j];
                         }
+                    }
+                }
                 for (int i = p; i < n; ++i) {
                     wData[i] = new double[p];
                 }