This section presents Java implementations of matrix computation algorithms and improves code included in an original document that presented the material together with implementations in C#.Net: Implementing Matrix Computation Algorithms, (2007), Mark Bishop, New England Testing Laboratory, Providence, RI.
Implementing Matrix Computation was developed with the following primary reference: Matrix Computations 3rd Edition by Gene H. Golub and Charles F. Van Loan (Johns Hopkins University Press, Baltimore, 1996).
The CPU implementations manage matrices as row-major float[m][n] arrays, where m = row count and n = column count. Vectors are represented as float[n] arrays. To be compatible with NVidia Cuda, the GPU implementations use column-major, vector representations of matrices, but convenience methods are provided in the classes for passing row-major float objects to the GPU methods. The implementations are literal translations of pseudo code and are not production-optimized.
In certain cases the methods return result objects that contain multiple result components arranged in object lists or arrays of float.
List<object>; GaussUpperTriangularFullPivot(float[ ][ ] A) float[ ][ ][ ] ParseLU(float[ ][ ] LU) List<object>; HouseholderCompactQRPivot(float[ ][ ] A) float[ ][ ][ ] LU(float A[ ][ ])
For example, the HouseholderCompactQRPivot (float[ ][ ] A) method returns a list<object> that includes an integer (that holds the matrix rank), an integer array (that holds a permutation matrix in vector form), and a float array (that holds pre-calculated beta factors such that Beta[j] = 2/(v[j]Transpose *v[j]) and Beta[length-1] = 0). This method also overwrites the A matrix with a compact form QR factorization.
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