Update test to move towards the previously found optimum
diff --git a/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/fitting/leastsquares/LevenbergMarquardtOptimizerTest.java b/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/fitting/leastsquares/LevenbergMarquardtOptimizerTest.java
index 4a76eb3..cdac94d 100644
--- a/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/fitting/leastsquares/LevenbergMarquardtOptimizerTest.java
+++ b/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/fitting/leastsquares/LevenbergMarquardtOptimizerTest.java
@@ -291,25 +291,40 @@
// First guess for the center's coordinates and radius.
final double[] init = { 118, 659, 115 };
- final Optimum optimum
- = optimizer.optimize(builder(circle).maxIterations(50).start(init).build());
+
+ final Optimum optimum = optimizer.optimize(
+ builder(circle).maxIterations(50).start(init).build());
+
final int numEval = optimum.getEvaluations();
Assert.assertTrue(numEval > 1);
// Build a new problem with a validator that amounts to cheating.
+
+ // Note we cannot return a fixed point.
+ // The optimiser relies on computing a predicted reduction in the cost
+ // function (preRed) and an actual reduction (actRed). The ratio between them must be
+ // non-zero to indicate the step reduced the cost function. If a threshold is not
+ // achieved then the step is rejected and the optimiser can cycle through many iterations
+ // not moving anywhere until alternative thresholds reduce to a level that terminate
+ // the cycle.
+ // Here we take the current point and move it towards an acceptable answer
+ // given the problem (the previous optimum). This should speed up the optimiser.
+ // This can still fail to reduce the iterations when the adjusted step moves
+ // to a sub-optimal position in the cost function.
final ParameterValidator cheatValidator
= new ParameterValidator() {
@Override
public RealVector validate(RealVector params) {
- // Cheat: return the optimum found previously.
- return optimum.getPoint();
+ // Cheat: Move towards the optimum found previously.
+ final RealVector direction = optimum.getPoint().subtract(params);
+ return params.add(direction.mapMultiply(0.75));
}
};
final Optimum cheatOptimum
= optimizer.optimize(builder(circle).maxIterations(50).start(init).parameterValidator(cheatValidator).build());
final int cheatNumEval = cheatOptimum.getEvaluations();
- Assert.assertTrue(cheatNumEval < numEval);
+ Assert.assertTrue("n=" + numEval + " nc=" + cheatNumEval, cheatNumEval < numEval);
// System.out.println("n=" + numEval + " nc=" + cheatNumEval);
}
