Solves: min_L (1/2) ||O o (Y - L)||F^2 + lambda * ||L||*
via iterative SVD soft-thresholding (Mazumder, Hastie, Tibshirani 2010).
Note: lambda is NOT normalized by |O|. Default lambda = 0.01 * sigma_max(Y).
Usage
soft_impute_cpp(Y, O, lambda, max_iter = 1000L, tol = 1e-05)
Arguments
- Y
Observed outcome matrix (N x T). Unobserved entries should be 0.
- O
Binary mask matrix (N x T): 1 = observed, 0 = missing (treated post).
- lambda
Nuclear norm penalty (soft-threshold on singular values).
- max_iter
Maximum iterations.
- tol
Convergence tolerance (relative Frobenius norm change).
Value
A numeric matrix of the same dimension as Y (N x T): the
completed low-rank matrix L that minimises the soft-thresholded
nuclear-norm objective.
