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From "Sujit Pal (JIRA)" <j...@apache.org>
Subject [jira] Commented: (MATH-230) Implement Sparse Matrix Support
Date Fri, 12 Dec 2008 03:02:44 GMT

    [ https://issues.apache.org/jira/browse/MATH-230?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12655868#action_12655868
] 

Sujit Pal commented on MATH-230:
--------------------------------

Apologies for not getting back earlier on this, I had a chance to look at the diff this morning.
I will work on making the SparseMatrix tests pass this evening and post the patch tomorrow
morning.

However, looking at the patch, I notice that this method is used to find the key to the internal
map for storing the entry:

+    private int computeKey(int row, int column) {
+        return row * columnDimension + column;
+    }

This effectively flattens out the matrix so a matrix like this:
1 2 3 4
5 6 7 8
9 10 11 12
is flattened out to:
1 2 3 4 5 6 7 8 9 10 11 12

Now if you wanted to look for entry (1,2) you look for entry (1*4 + 2) = 6. So we will always
get a unique key for a given matrix position, given that by specifying the row and column
dimension we are always specifying a fixed size rectangular matrix. 

This is quite beautiful and clever (note to Ismael: Thanks for doing this, and I wish I had
thought of this :-)). 

But the question is: do we still need an open-addressed map structure? It seems to me that
we can now just represent the sparse matrix internally with a Map<Integer,Double>? That
way we don't even have to think about whether we want to put it as an inner class or in utils.

Thoughts?


> Implement Sparse Matrix Support
> -------------------------------
>
>                 Key: MATH-230
>                 URL: https://issues.apache.org/jira/browse/MATH-230
>             Project: Commons Math
>          Issue Type: Improvement
>    Affects Versions: 2.0
>         Environment: N/A
>            Reporter: Sujit Pal
>            Assignee: Luc Maisonobe
>            Priority: Minor
>             Fix For: 2.0
>
>         Attachments: math-230.diff, patch.txt, RealMatrixImplPerformanceTest.java, SparseRealMatrixImpl.java,
SparseRealMatrixImplTest.java
>
>
> I needed a way to deal with large sparse matrices using commons-math RealMatrix, so I
implemented it. The SparseRealMatrixImpl is a subclass of RealMatrixImpl, and the backing
data structure is a Map<Point,Double>, where Point is a struct like inner-class which
exposes two int parameters row and column. I had to make some changes to the existing components
to keep the code for SparseRealMatrixImpl clean. Here are the details.
> 1) RealMatrix.java:
>    - added a new method setEntry(int, int, double) to set data into a matrix
> 2) RealMatrixImpl.java:
>    - changed all internal calls to data[i][j] to getEntry(i,j).
>    - for some methods such as add(), subtract(), premultiply(), etc, there
>      was code that checked for ClassCastException and had two versions,
>      one for a generic RealMatrix and one for a RealMatrixImpl. This has
>      been changed to have only one that operates on a RealMatrix. The 
>      result is something like auto-type casting. So if:
>        RealMatrixImpl.add(RealMatrix) returns a RealMatrixImpl
>        SparseRealMatrixImpl.add(RealMatrix) returns a SparseRealMatrixImpl
> 3) SparseRealMatrixImpl added as a subclass of RealMatrixImpl.
> 4) LUDecompositionImpl changed to use a clone of the passed in RealMatrix
>    instead of its data[][] block, and now it uses clone.getEntry(row,col)
>    calls instead of data[row][col] calls.
> 5) LUDecompositionImpl returned RealMatrixImpl for getL(), getU(), getP()
>    and solve(). It now returns the same RealMatrix impl that is passed 
>    in through its constructor for these methods.
> 6) New test for SparseRealMatrixImpl, mimics the tests in RealMatrixImplTest,
> 7) New static method to create SparseRealMatrixImpl out of a double[][] in
>    MatrixUtils.createSparseRealMatrix().
>    but using SparseRealMatrixImpl.
> 8) Verified that all JUnit tests pass.

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