diff --git a/src/main/java/org/apache/commons/math3/linear/EigenDecomposition.java b/src/main/java/org/apache/commons/math3/linear/EigenDecomposition.java
index aa9672531..ceb278e63 100644
--- a/src/main/java/org/apache/commons/math3/linear/EigenDecomposition.java
+++ b/src/main/java/org/apache/commons/math3/linear/EigenDecomposition.java
@@ -75,6 +75,8 @@ import org.apache.commons.math3.util.FastMath;
  * @since 2.0 (changed to concrete class in 3.0)
  */
 public class EigenDecomposition {
+    /** Internally used epsilon criteria. */
+    private static final double EPSILON = 1e-12;
     /** Maximum number of iterations accepted in the implicit QL transformation */
     private byte maxIter = 30;
     /** Main diagonal of the tridiagonal matrix. */
@@ -99,9 +101,6 @@ public class EigenDecomposition {
     /** Cached value of Vt. */
     private RealMatrix cachedVt;
 
-    /** Internally used epsilon criteria. */
-    private final double epsilon = 1e-12;
-
     /**
      * Calculates the eigen decomposition of the given real matrix.
      * <p>
@@ -246,9 +245,9 @@ public class EigenDecomposition {
             cachedD = MatrixUtils.createRealDiagonalMatrix(realEigenvalues);
 
             for (int i = 0; i < imagEigenvalues.length; i++) {
-                if (Precision.compareTo(imagEigenvalues[i], 0.0, epsilon) > 0) {
+                if (Precision.compareTo(imagEigenvalues[i], 0.0, EPSILON) > 0) {
                     cachedD.setEntry(i, i+1, imagEigenvalues[i]);
-                } else if (Precision.compareTo(imagEigenvalues[i], 0.0, epsilon) < 0) {
+                } else if (Precision.compareTo(imagEigenvalues[i], 0.0, EPSILON) < 0) {
                     cachedD.setEntry(i, i-1, imagEigenvalues[i]);
                 }
             }
@@ -291,7 +290,7 @@ public class EigenDecomposition {
      */
     public boolean hasComplexEigenvalues() {
         for (int i = 0; i < imagEigenvalues.length; i++) {
-            if (!Precision.equals(imagEigenvalues[i], 0.0, epsilon)) {
+            if (!Precision.equals(imagEigenvalues[i], 0.0, EPSILON)) {
                 return true;
             }
         }
@@ -726,7 +725,7 @@ public class EigenDecomposition {
 
         for (int i = 0; i < realEigenvalues.length; i++) {
             if (i == (realEigenvalues.length - 1) ||
-                Precision.equals(matT[i + 1][i], 0.0, epsilon)) {
+                Precision.equals(matT[i + 1][i], 0.0, EPSILON)) {
                 realEigenvalues[i] = matT[i][i];
             } else {
                 final double x = matT[i + 1][i + 1];
@@ -777,7 +776,7 @@ public class EigenDecomposition {
         }
 
         // we can not handle a matrix with zero norm
-        if (Precision.equals(norm, 0.0, epsilon)) {
+        if (Precision.equals(norm, 0.0, EPSILON)) {
            throw new MathArithmeticException(LocalizedFormats.ZERO_NORM);
         }
 
@@ -801,7 +800,7 @@ public class EigenDecomposition {
                     for (int j = l; j <= idx; j++) {
                         r = r + matrixT[i][j] * matrixT[j][idx];
                     }
-                    if (Precision.compareTo(imagEigenvalues[i], 0.0, epsilon) < 0.0) {
+                    if (Precision.compareTo(imagEigenvalues[i], 0.0, EPSILON) < 0.0) {
                         z = w;
                         s = r;
                     } else {
@@ -863,7 +862,7 @@ public class EigenDecomposition {
                     }
                     double w = matrixT[i][i] - p;
 
-                    if (Precision.compareTo(imagEigenvalues[i], 0.0, epsilon) < 0.0) {
+                    if (Precision.compareTo(imagEigenvalues[i], 0.0, EPSILON) < 0.0) {
                         z = w;
                         r = ra;
                         s = sa;