Sparse matrix storage
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Supervisor |
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Suitable for |
MSc in Computer Science
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Abstract
Many matrices used in scientific computing applications are large (100s of thousands of rows) and sparse (typically around 10 non-zeros per row). Storing the whole matrix (including the zeros) is infeasible due to memory constraints. Instead, the matrix is stored in sparse format: only the non-zero entries and the location of these entries are stored. This clearly affects the implementation of operations on this matrix, such as arithmetic operations and calculation of the determinant. A further complication is that non-zeros may be added to the matrix at any time, and so the sparsity pattern changes dynamically. In this project, an efficient sparse storage format will be implemented, together with a suite of operations using this sparse storage format.
Prerequisite: first year linear algebra. Continuous mathematics would be desirable.