ADMM Lasso

@Authors: Aleksandar Armacki and Lidija Fodor @Affiliation: Faculty of Sciences, University of Novi Sad, Serbia

This work is supported by the I-BiDaaS project, funded by the European Commission under Grant Agreement No. 780787.

class dislib.optimization.admm.base.ADMM(loss_fn, k, rho=1, max_iter=100, rtol=0.01, atol=0.0001, verbose=False)[source]

Bases: BaseEstimator

Alternating Direction Method of Multipliers (ADMM) solver. ADMM is renowned for being well suited to the distributed settings 1, for its guaranteed convergence and general robustness with respect to the parameters. Additionally, the algorithm has a generic form that can be easily adapted to a wide range of machine learning problems with only minor tweaks in the code.

  • loss_fn (func) – Loss function.

  • k (float) – Soft thresholding value.

  • rho (float, optional (default=1)) – The penalty parameter for constraint violation.

  • max_iter (int, optional (default=100)) – Maximum number of iterations to perform.

  • atol (float, optional (default=1e-4)) – The absolute tolerance used to calculate the early stop criterion.

  • rtol (float, optional (default=1e-2)) – The relative tolerance used to calculate the early stop criterion.

  • verbose (boolean, optional (default=False)) – Whether to print information about the optimization process.

  • z (ds-array shape=(1, n_features)) – Computed z.

  • n_iter (int) – Number of iterations performed.

  • converged (boolean) – Whether the optimization converged.



S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein (2011). Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. In Foundations and Trends in Machine Learning, 3(1):1–122.

fit(x, y)[source]

Fits the model with training data.

  • x (ds-array, shape=(n_samples, n_features)) – Training samples.

  • y (ds-array, shape=(n_samples, 1)) – Class labels of x.



Return type