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fixed an ArrayIndexOutOfBoundsException 

Kudos to Dimitri who debugged this mess of fortran/java array indices translation
JIRA: MATH-308

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@832577 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Luc Maisonobe 2009-11-03 22:04:08 +00:00
parent 0596e3145c
commit c78a0e3b00
3 changed files with 48 additions and 6 deletions
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16
src/main/java/org/apache/commons/math/linear/EigenDecompositionImpl.java
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@ -595,8 +595,12 @@ public class EigenDecompositionImpl implements EigenDecomposition {
}
final double dCurrent = main[m - 1];
work[lowerStart + m - 1] = dCurrent - eCurrent;
work[upperStart + m - 1] = dCurrent + eCurrent;
final double lower = dCurrent - eCurrent;
work[lowerStart + m - 1] = lower;
lowerSpectra = Math.min(lowerSpectra, lower);
final double upper = dCurrent + eCurrent;
work[upperStart + m - 1] = upper;
upperSpectra = Math.max(upperSpectra, upper);
minPivot = MathUtils.SAFE_MIN * Math.max(1.0, eMax * eMax);
}
@ -647,7 +651,7 @@ public class EigenDecompositionImpl implements EigenDecomposition {
tau = (range[1] - range[0]) * MathUtils.EPSILON * n + 2 * minPivot;
// decompose T&lambda;I as LDL<sup>T</sup>
// decompose T-&lambda;I as LDL<sup>T</sup>
ldlTDecomposition(lambda, begin, n);
// apply general dqd/dqds method
@ -899,8 +903,8 @@ public class EigenDecompositionImpl implements EigenDecomposition {
diagMax = work[4 * i0];
offDiagMin = work[4 * i0 + 2];
double previousEMin = work[4 * i0 + 3];
for (int i = 4 * i0; i < 4 * n0 - 11; i += 4) {
if ((work[i + 3] <= TOLERANCE_2 * work[i]) &&
for (int i = 4 * i0; i < 4 * n0 - 16; i += 4) {
if ((work[i + 3] <= TOLERANCE_2 * work[i]) ||
(work[i + 2] <= TOLERANCE_2 * sigma)) {
// insert a split
work[i + 2] = -sigma;
@ -1537,7 +1541,7 @@ public class EigenDecompositionImpl implements EigenDecomposition {
double a2 = (work[np - 8] / b2) * (1 + work[np - 4] / b1);
// approximate contribution to norm squared from i < nn-2.
if (end - start > 2) {
if (end - start > 3) {
b2 = work[nn - 13] / work[nn - 15];
a2 = a2 + b2;
for (int i4 = nn - 17; i4 >= 4 * start + 2 + pingPong; i4 -= 4) {

4
src/site/xdoc/changes.xml
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@ -39,6 +39,10 @@ The <action> type attribute can be add,update,fix,remove.
</properties>
<body>
<release version="2.1" date="TBD" description="TBD">
<action dev="luc" type="fix" issue="MATH-308" due-to="Dimitri Pourbaix">
Fixed an ArrayIndexOutOfBoundsException in eigen decomposition. Kudos to Dimitri
for debugging this, it was really difficult.
</action>
<action dev="psteitz" type="fix" issue="MATH-309" due-to="Mikkel Meyer Andersen">
Fixed parameter test in RandomDataImpl#nextExponential. The method now throws
IllegalArgumentException for mean = 0.

34
src/test/java/org/apache/commons/math/linear/EigenDecompositionImplTest.java
View File

@ -108,6 +108,40 @@ public class EigenDecompositionImplTest extends TestCase {
assertEquals(0.1, ed.getRealEigenvalue(3), 1.0e-15);
}
// the following test triggered an ArrayIndexOutOfBoundsException in commons-math 2.0
public void testMath308() {
double[] mainTridiagonal = {
22.330154644539597, 46.65485522478641, 17.393672330044705, 54.46687435351116, 80.17800767709437
};
double[] secondaryTridiagonal = {
13.04450406501361, -5.977590941539671, 2.9040909856707517, 7.1570352792841225
};
// the reference values have been computed using routine DSTEMR
// from the fortran library LAPACK version 3.2.1
double[] refEigenValues = {
82.044413207204002, 53.456697699894512, 52.536278520113882, 18.847969733754262, 14.138204224043099
};
RealVector[] refEigenVectors = {
new ArrayRealVector(new double[] { -0.000462690386766, -0.002118073109055, 0.011530080757413, 0.252322434584915, 0.967572088232592 }),
new ArrayRealVector(new double[] { 0.314647769490148, 0.750806415553905, -0.167700312025760, -0.537092972407375, 0.143854968127780 }),
new ArrayRealVector(new double[] { 0.222368839324646, 0.514921891363332, -0.021377019336614, 0.801196801016305, -0.207446991247740 }),
new ArrayRealVector(new double[] { 0.713933751051495, -0.190582113553930, 0.671410443368332, -0.056056055955050, 0.006541576993581 }),
new ArrayRealVector(new double[] { 0.584677060845929, -0.367177264979103, -0.721453187784497, 0.052971054621812, -0.005740715188257 })
};
EigenDecomposition decomposition =
new EigenDecompositionImpl(mainTridiagonal, secondaryTridiagonal, MathUtils.SAFE_MIN);
double[] eigenValues = decomposition.getRealEigenvalues();
for (int i = 0; i < refEigenValues.length; ++i) {
assertEquals(refEigenValues[i], eigenValues[i], 1.0e-5);
assertEquals(0, refEigenVectors[i].subtract(decomposition.getEigenvector(i)).getNorm(), 2.0e-7);
}
}
/** test a matrix already in tridiagonal form. */
public void testTridiagonal() {
Random r = new Random(4366663527842l);