MATH-1144

Point must be retrieved after the call to "evaluate" (to ensure that
the validated parameters are actually used by the optimizer).

src/main/java/org/apache/commons/math3/fitting/leastsquares/GaussNewtonOptimizer.java

@@ -230,6 +230,7 @@ public class GaussNewtonOptimizer implements LeastSquaresOptimizer {
             current = lsp.evaluate(currentPoint);
             final RealVector currentResiduals = current.getResiduals();
             final RealMatrix weightedJacobian = current.getJacobian();
+            currentPoint = current.getPoint();
 
             // Check convergence.
             if (previous != null) {

src/main/java/org/apache/commons/math3/fitting/leastsquares/LevenbergMarquardtOptimizer.java

@@ -294,16 +294,14 @@ public class LevenbergMarquardtOptimizer implements LeastSquaresOptimizer {
 
     /** {@inheritDoc} */
     public Optimum optimize(final LeastSquaresProblem problem) {
-        //pull in relevant data from the problem as locals
+        // Pull in relevant data from the problem as locals.
         final int nR = problem.getObservationSize(); // Number of observed data.
         final int nC = problem.getParameterSize(); // Number of parameters.
-        final double[] currentPoint = problem.getStart().toArray();
-        //counters
+        // Counters.
         final Incrementor iterationCounter = problem.getIterationCounter();
         final Incrementor evaluationCounter = problem.getEvaluationCounter();
-        //convergence criterion
-        final ConvergenceChecker<Evaluation> checker
-                = problem.getConvergenceChecker();
+        // Convergence criterion.
+        final ConvergenceChecker<Evaluation> checker = problem.getConvergenceChecker();
 
         // arrays shared with the other private methods
         final int solvedCols  = FastMath.min(nR, nC);
@@ -327,9 +325,10 @@ public class LevenbergMarquardtOptimizer implements LeastSquaresOptimizer {
         // Evaluate the function at the starting point and calculate its norm.
         evaluationCounter.incrementCount();
         //value will be reassigned in the loop
-        Evaluation current = problem.evaluate(new ArrayRealVector(currentPoint));
+        Evaluation current = problem.evaluate(problem.getStart());
         double[] currentResiduals = current.getResiduals().toArray();
         double currentCost = current.getCost();
+        double[] currentPoint = current.getPoint().toArray();
 
         // Outer loop.
         boolean firstIteration = true;
@@ -447,6 +446,7 @@ public class LevenbergMarquardtOptimizer implements LeastSquaresOptimizer {
                 current = problem.evaluate(new ArrayRealVector(currentPoint));
                 currentResiduals = current.getResiduals().toArray();
                 currentCost = current.getCost();
+                currentPoint = current.getPoint().toArray();
 
                 // compute the scaled actual reduction
                 double actRed = -1.0;

src/test/java/org/apache/commons/math3/fitting/leastsquares/LevenbergMarquardtOptimizerTest.java

@@ -307,6 +307,7 @@ public class LevenbergMarquardtOptimizerTest
             = optimizer.optimize(builder(circle).maxIterations(50).start(init).parameterValidator(cheatValidator).build());
         final int cheatNumEval = cheatOptimum.getEvaluations();
         Assert.assertTrue(cheatNumEval < numEval);
+        System.out.println("n=" + numEval + " nc=" + cheatNumEval);
     }
 
     @Test