Hi.
Le lun. 10 juin 2019 à 11:01, Marshall Wice <[email protected]> a écrit :
>
> Hello,
>
> I am using the LevenbergMarquardtOptimizer and am receiving very strange
> results. Running the below code, it appears as though the
> LevenbergMarquardtOptimizer is diverging rather than converging.
Sorry that you encounter problems with that code. :-(
> In Matlab I run the equivalent code, and the algorithm correctly converges.
> Is there some bounds that can be set on the algorithm so it does not diverge?
Bounds are not supported in this implementation.
Users can set them indirectly through a "logit" transform on the model
parameters.
>
> Java Code:
> RealVector theta_pr = createZerosVector(9);
> double[] gravity = new double[selectedAccData.getColumnDimension()];
> Arrays.fill(gravity, Math.pow(9.81744, 2));
>
> LeastSquaresProblem problem = new LeastSquaresBuilder()
> .start(new double[]{0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01})
> .model(costFunc)
> .target(gravity)
> .lazyEvaluation(false)
> .maxEvaluations(150000)
> .maxIterations(6000)
> .build();
>
> LeastSquaresOptimizer.Optimum optimum = new LevenbergMarquardtOptimizer()
> .withCostRelativeTolerance(1.0e-10)
> .optimize(problem);
> private MultivariateJacobianFunction costFunc = new
> MultivariateJacobianFunction() {
> @Override
> public Pair<RealVector, RealMatrix> value(RealVector point) {
>
> double E1 = point.getEntry(0);
> double E2 = point.getEntry(1);
> double E3 = point.getEntry(2);
> double E4 = point.getEntry(3);
> double E5 = point.getEntry(4);
> double E6 = point.getEntry(5);
> double E7 = point.getEntry(6);
> double E8 = point.getEntry(7);
> double E9 = point.getEntry(8);
>
> RealVector value = new
> ArrayRealVector(selectedAccData.getColumnDimension());
> RealMatrix jacobian = new
> Array2DRowRealMatrix(selectedAccData.getColumnDimension(), 9);
>
> RealMatrix misalignmentMatrix = MatrixUtils.createRealMatrix(new
> double[][]{{1, -E1, E2}, {0, 1, -E3}, {0 ,0, 1}});
> RealMatrix scalingMatrix = MatrixUtils.createRealDiagonalMatrix(new
> double[]{E4, E5, E6});
> RealMatrix dMatrix = MatrixUtils.createRealDiagonalMatrix(new
> double[]{E7, E8, E9});
> RealMatrix oMatrix =
> createOnesMatrix(3,selectedAccData.getColumnDimension());
> RealMatrix a_bar =
> misalignmentMatrix.multiply(scalingMatrix).multiply(selectedAccData).subtract(dMatrix.multiply(oMatrix));
>
> for (int i = 0; i < selectedAccData.getColumnDimension(); ++i) {
>
> double modelI =
> (Math.pow(a_bar.getEntry(0,i),2)+Math.pow(a_bar.getEntry(1,i),2)+Math.pow(a_bar.getEntry(2,i),2));
> value.setEntry(i, modelI);
>
> double a1 = selectedAccData.getEntry(0,i);
> double a2 = selectedAccData.getEntry(1,i);
> double a3 = selectedAccData.getEntry(2,i);
>
> jacobian.setEntry(i, 0, 2*E5*a2*(E7 - E4*a1 + E1*E5*a2 -
> E2*E6*a3));
> jacobian.setEntry(i, 1, -2*E6*a3*(E7 - E4*a1 + E1*E5*a2 -
> E2*E6*a3));
> jacobian.setEntry(i, 2, 2*E6*a3*(E8 - E5*a2 + E3*E6*a3));
> jacobian.setEntry(i, 3, -2*a1*(E7 - E4*a1 + E1*E5*a2 - E2*E6*a3));
> jacobian.setEntry(i, 4, 2*E1*a2*(E7 - E4*a1 + E1*E5*a2 -
> E2*E6*a3) - 2*a2*(E8 - E5*a2 + E3*E6*a3));
> jacobian.setEntry(i, 5, 2*E3*a3*(E8 - E5*a2 + E3*E6*a3) -
> 2*E2*a3*(E7 - E4*a1 + E1*E5*a2 - E2*E6*a3) - 2*a3*(E9 - E6*a3));
> jacobian.setEntry(i, 6, 2*E7 - 2*E4*a1 + 2*E1*E5*a2 - 2*E2*E6*a3);
> jacobian.setEntry(i, 7, 2*E8 - 2*E5*a2 + 2*E3*E6*a3);
> jacobian.setEntry(i, 8, 2*E9 - 2*E6*a3);
> }
>
> return new Pair<>(value, jacobian);
> }
> };
The above calls methods that are not defined in this code excerpt.
Bette is to file a report on the issue-tracking system:
https://issues.apache.org/jira/projects/MATH
There you should attached a minimal working example that shows
the problem, preferably in the form of a unit test, like those that
are already provided:
https://gitbox.apache.org/repos/asf?p=commons-math.git;a=tree;f=src/test/java/org/apache/commons/math4/fitting/leastsquares
> Equivalent Matlab Code:
> theta_pr = [0, 0, 0, 0, 0, 0, 0, 0, 0];
> ObjectiveFunction = @(theta_pr) costFunc(theta_pr, selectedAccData);
> options = optimset('MaxFunEvals', 150000, 'MaxIter', 6000, 'TolFun',
> 10^(-10));
> [theta_pr rsnorm] = lsqnonlin(ObjectiveFunction, theta_pr, [], [], options);
> function [ res_vector ] = costFunc( E, a_hat )
>
> misalignmentMatrix = [1, -E(1), E(2); 0, 1, -E(3); 0, 0, 1];
> scalingMtrix = diag([E(4), E(5), E(6)]);
>
> a_bar = misalignmentMatrix*scalingMtrix*(a_hat) - (diag([E(7), E(8),
> E(9)])*ones(3, length(a_hat)));
>
> residuals = zeros(length(a_bar(1,:)), 1);
>
> for i = 1:length(a_bar(1,:))
> residuals(i,1) = 9.81744^2 - (a_bar(1,i)^2 + a_bar(2,i)^2 + a_bar(3,i)^2)
> end
>
> res_vector = residuals;
>
> end
>
> If this is not the correct forum to be asking this, or if there is a better
> way to communicate and share my code, please let me know.
Cf. above.
Thanks for the report,
Gilles
>
> Thank you,
>
> Marshall
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