sparse_ho.algo.ImplicitForward¶
- class sparse_ho.algo.ImplicitForward(tol_jac=0.001, max_iter=100, n_iter_jac=100, use_stop_crit=True, verbose=False)¶
Algorithm to compute the hypergradient using implicit forward differentiation.
First the algorithm computes the regression coefficients. Then the iterations of the forward differentiation are applied to compute the Jacobian.
- Parameters
- tol_jac: float
Tolerance for the Jacobian computation.
- max_iter: int
Maximum number of iterations for the inner solver.
- n_iter_jac: int
Maximum number of iterations for the Jacobian computation.
- use_stop_crit: bool, optional (default=True)
Use stopping criterion in hypergradient computation. If False, run to maximum number of iterations.
- verbose: bool, optional (default=False)
Verbosity of the algorithm.
- __init__(tol_jac=0.001, max_iter=100, n_iter_jac=100, use_stop_crit=True, verbose=False)¶
Methods
__init__
([tol_jac, max_iter, n_iter_jac, ...])compute_beta_grad
(X, y, log_alpha, model, ...)get_beta_jac
(X, y, log_alpha, model, ...[, ...])Compute beta and hypergradient using implicit forward differentiation.