Optimal initializer for SiMLR — optimal_simlr

This function generates multiple random candidate U matrices, evaluates their associated energy (without gradient descent), and returns the best-scoring initialization. A seed parameter ensures reproducibility across runs.

optimal_simlr_initializer(
  data_matrices,
  n_init = 10,
  basisK,
  energyType = "acc",
  domainMatrices = NULL,
  domainLambdas = NULL,
  verbose = TRUE,
  seed = NULL
)

Arguments

data_matrices: A list of modality-specific data matrices.
n_init: Number of random initializations to try (default = 10).
basisK: Number of basis components (columns in U).
energyType: Energy function to evaluate.
domainMatrices: Optional list of domain priors (same length as data_matrices).
domainLambdas: Optional vector of domain weights.
verbose: Logical, whether to print progress.
seed: Optional numeric seed for reproducibility (default = NULL, no seed set).

Value

A list with elements:

bestU: List of U matrices (one per modality) for the best initialization.
bestV: List of corresponding V matrices.
bestEnergy: Mean energy for the selected initialization.