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From "Luc Maisonobe (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MATH-1101) QR and Rank-revealing QR fail to find a least-squares solution
Date Fri, 21 Feb 2014 13:51:19 GMT

    [ https://issues.apache.org/jira/browse/MATH-1101?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13908324#comment-13908324
] 

Luc Maisonobe commented on MATH-1101:
-------------------------------------

I am not sure to understand. The matrix is exactly singular here, which is correctly identified
if you pass a threshold of about 2e-16 to the QRDecomposition constructor (without passing
it, the default threshold is an exact 0). With the default 0 threshold, the last diagonal
element is really small (8.88e-16) and using it implies computing big values.

As all dimensions are 3, I don't understand were you intend to have a least squares solution.
What is attempted here seems to be computing a full linear solution of a singular problem.

> QR and Rank-revealing QR fail to find a least-squares solution
> --------------------------------------------------------------
>
>                 Key: MATH-1101
>                 URL: https://issues.apache.org/jira/browse/MATH-1101
>             Project: Commons Math
>          Issue Type: Bug
>    Affects Versions: 3.2
>            Reporter: Roman Werpachowski
>              Labels: solver
>         Attachments: math-1101-bug.java
>
>
> QR and RRQR (rank-revealing) algorithms fail to find a least-squares solution in some
cases.
> The following code:
> final RealMatrix A = new BlockRealMatrix(3, 3);
>         A.setEntry(0, 0, 1);
>         A.setEntry(0, 1, 6);
>         A.setEntry(0, 2, 4);
>         A.setEntry(1, 0, 2);
>         A.setEntry(1, 1, 4);
>         A.setEntry(1, 2, -1);
>         A.setEntry(2, 0, -1);
>         A.setEntry(2, 1, 2);
>         A.setEntry(2, 2, 5);
>         final RealVector b = new ArrayRealVector(new double[]{5, 6, 1});
>         final QRDecomposition qrDecomposition = new QRDecomposition(A);
>         final RRQRDecomposition rrqrDecomposition = new RRQRDecomposition(A);
>         final SingularValueDecomposition svd = new SingularValueDecomposition(A);
>         final RealVector xQR = qrDecomposition.getSolver().solve(b);
>         System.out.printf("QR solution: %s\n", xQR.toString());
>         final RealVector xRRQR = rrqrDecomposition.getSolver().solve(b);
>         System.out.printf("RRSQ solution: %s\n", xRRQR.toString());
>         final RealVector xSVD = svd.getSolver().solve(b);
>         System.out.printf("SVD solution: %s\n", xSVD.toString());
> produces
> QR solution: {-3,575,212,378,628,897; 1,462,586,882,166,368; -1,300,077,228,592,326.5}
> RRSQ solution: {5,200,308,914,369,308; -2,127,399,101,332,898; 1,891,021,423,407,021}
> SVD solution: {0.5050344462; 1.0206677266; -0.2405935347}
> Showing that QR and RRQR algorithms fail to find the least-squares solution. This can
also be verified by calculating the dot product between columns of A and A*x - b:
> // x = xQR, xRRQR or xSVD
> final RealVector r = A.operate(x).subtract(b);
>         for (int i = 0; i < x.getDimension(); ++i) {
>             final RealVector columnVector = A.getColumnVector(i);
>             assertEquals(name, 0.0, r.dotProduct(columnVector), tolerance);
>         }
> Only SVD method passes this test with decent tolerance (1E-14 or so).



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