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ar.svd.svds
Compute partial singular value decomposition of a sparse matrix.
Syntax
ar.svd.svds(matvec, matvecT, m, n, k, options?)
Description
Compute partial singular value decomposition of a sparse matrix. Finds the k largest or smallest singular values and optionally the corresponding left and right singular vectors.
Parameters
| Name | Description |
|---|---|
| matvec | - Function computing y = A*x (m×n matrix applied to n-vector) |
| matvecT | - Function computing y = A^T*x (n×m matrix applied to m-vector) |
| m | - Number of rows in the matrix |
| n | - Number of columns in the matrix |
| k | - Number of singular values to compute |
| options(optional) | - Solver options |
Returns
Promise<SvdsResult>