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improve both numerical accuracy and speed by using optimized loops 

in reversed row order (i.e. from higher orders to lower orders) directly
on matrix data.

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@789157 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Luc Maisonobe 2009-06-28 21:51:09 +00:00
parent b5395fd802
commit 43bc08d7c3
1 changed files with 36 additions and 135 deletions
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167
src/java/org/apache/commons/math/ode/sampling/NordsieckStepInterpolator.java
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@ -23,9 +23,6 @@ import java.io.ObjectOutput;
import java.util.Arrays;
import org.apache.commons.math.linear.Array2DRowRealMatrix;
import org.apache.commons.math.linear.DefaultRealMatrixChangingVisitor;
import org.apache.commons.math.linear.RealMatrix;
import org.apache.commons.math.linear.RealMatrixPreservingVisitor;
/**
* This class implements an interpolator for integrators using Nordsieck representation.
@ -59,7 +56,7 @@ public class NordsieckStepInterpolator extends AbstractStepInterpolator {
private double[] scaled;
/** Nordsieck vector. */
private RealMatrix nordsieck;
private Array2DRowRealMatrix nordsieck;
/** Simple constructor.
* This constructor builds an instance that is not usable yet, the
@ -84,7 +81,7 @@ public class NordsieckStepInterpolator extends AbstractStepInterpolator {
scaled = interpolator.scaled.clone();
}
if (interpolator.nordsieck != null) {
nordsieck = interpolator.nordsieck.copy();
nordsieck = new Array2DRowRealMatrix(interpolator.nordsieck.getDataRef(), true);
}
}
@ -117,7 +114,7 @@ public class NordsieckStepInterpolator extends AbstractStepInterpolator {
* nordsieck vector
*/
public void reinitialize(final double referenceTime, final double scalingH,
final double[] scaled, final RealMatrix nordsieck) {
final double[] scaled, final Array2DRowRealMatrix nordsieck) {
this.referenceTime = referenceTime;
this.scalingH = scalingH;
this.scaled = scaled;
@ -134,96 +131,54 @@ public class NordsieckStepInterpolator extends AbstractStepInterpolator {
* @param scalingH new step size to use in the scaled and nordsieck arrays
*/
public void rescale(final double scalingH) {
final double ratio = scalingH / this.scalingH;
for (int i = 0; i < scaled.length; ++i) {
scaled[i] *= ratio;
}
nordsieck.walkInOptimizedOrder(new Rescaler(ratio));
final double[][] nData = nordsieck.getDataRef();
double power = ratio;
for (int i = 0; i < nData.length; ++i) {
power *= ratio;
final double[] nDataI = nData[i];
for (int j = 0; j < nDataI.length; ++j) {
nDataI[j] *= power;
}
}
this.scalingH = scalingH;
}
/** {@inheritDoc} */
@Override
protected void computeInterpolatedStateAndDerivatives(final double theta, final double oneMinusThetaH) {
final double x = interpolatedTime - referenceTime;
nordsieck.walkInOptimizedOrder(new StateEstimator(x, x / scalingH));
}
final double normalizedAbscissa = x / scalingH;
/** State estimator. */
private class StateEstimator implements RealMatrixPreservingVisitor {
/** Scaling factor for derivative. */
private final double scale;
/** First order power. */
private final double lowPower;
/** High order powers. */
private final double[] highPowers;
/** Simple constructor.
* @param scale scaling factor for derivative
* @param theta normalized interpolation abscissa within the step
*/
public StateEstimator(final double scale, final double theta) {
this.scale = scale;
lowPower = theta;
highPowers = new double[nordsieck.getRowDimension()];
double thetaN = theta;
for (int i = 0; i < highPowers.length; ++i) {
thetaN *= theta;
highPowers[i] = thetaN;
}
}
/** {@inheritDoc} */
public void start(int rows, int columns,
int startRow, int endRow, int startColumn, int endColumn) {
Arrays.fill(interpolatedState, 0.0);
Arrays.fill(interpolatedDerivatives, 0.0);
// apply Taylor formula for high order to low order,
// for the sake of numerical accuracy
final double[][] nData = nordsieck.getDataRef();
for (int i = nData.length - 1; i >= 0; --i) {
final int order = i + 2;
final double[] nDataI = nData[i];
final double power = Math.pow(normalizedAbscissa, order);
for (int j = 0; j < nDataI.length; ++j) {
final double d = nDataI[j] * power;
interpolatedState[j] += d;
interpolatedDerivatives[j] += order * d;
}
}
/** {@inheritDoc} */
public void visit(int row, int column, double value) {
final double d = value * highPowers[row];
interpolatedState[column] += d;
interpolatedDerivatives[column] += (row + 2) * d;
}
/** {@inheritDoc} */
public double end() {
for (int j = 0; j < currentState.length; ++j) {
interpolatedState[j] += currentState[j] + scaled[j] * lowPower;
interpolatedState[j] += currentState[j] + scaled[j] * normalizedAbscissa;
interpolatedDerivatives[j] =
(interpolatedDerivatives[j] + scaled[j] * lowPower) / scale;
}
return 0;
}
}
/** Visitor rescaling the Nordsieck vector. */
private class Rescaler extends DefaultRealMatrixChangingVisitor {
/** Powers of the rescaling ratio. */
private final double[] powers;
/** Simple constructor.
* @param ratio rescaling ratio
*/
public Rescaler(final double ratio) {
powers = new double[nordsieck.getRowDimension()];
double f = ratio;
for (int i = 0; i < powers.length; ++i) {
f *= ratio;
powers[i] = f;
}
}
/** {@inheritDoc} */
@Override
public double visit(final int row, final int column, final double value) {
return value * powers[row];
(interpolatedDerivatives[j] + scaled[j] * normalizedAbscissa) / x;
}
}
@ -232,34 +187,7 @@ public class NordsieckStepInterpolator extends AbstractStepInterpolator {
@Override
public void writeExternal(final ObjectOutput out)
throws IOException {
// save the state of the base class
writeBaseExternal(out);
// save the local attributes
final int n = (currentState == null) ? -1 : currentState.length;
if (scaled == null) {
out.writeBoolean(false);
} else {
out.writeBoolean(true);
for (int j = 0; j < n; ++j) {
out.writeDouble(scaled[j]);
}
}
if (nordsieck == null) {
out.writeBoolean(false);
} else {
out.writeBoolean(true);
final int rows = nordsieck.getRowDimension();
out.writeInt(rows);
for (int i = 0; i < rows; ++i) {
for (int j = 0; j < n; ++j) {
out.writeDouble(nordsieck.getEntry(i, j));
}
}
}
}
/** {@inheritDoc} */
@ -270,34 +198,7 @@ public class NordsieckStepInterpolator extends AbstractStepInterpolator {
// read the base class
final double t = readBaseExternal(in);
// read the local attributes
final int n = (currentState == null) ? -1 : currentState.length;
final boolean hasScaled = in.readBoolean();
if (hasScaled) {
scaled = new double[n];
for (int j = 0; j < n; ++j) {
scaled[j] = in.readDouble();
}
} else {
scaled = null;
}
final boolean hasNordsieck = in.readBoolean();
if (hasNordsieck) {
final int rows = in.readInt();
final double[][] nData = new double[rows][n];
for (int i = 0; i < rows; ++i) {
final double[] nI = nData[i];
for (int j = 0; j < n; ++j) {
nI[j] = in.readDouble();
}
}
nordsieck = new Array2DRowRealMatrix(nData, false);
} else {
nordsieck = null;
}
if (hasScaled && hasNordsieck) {
if ((scaled != null) && (nordsieck != null)) {
// we can now set the interpolated time and state
setInterpolatedTime(t);
}