Computational TipsΒΆ

The generic splicing technique certifiably guarantees the best subset can be selected in a polynomial time. In practice, the computational efficiency can be improved to handle large scale datasets. The tips for computational improvement are applicable for:

  1. ultra-high dimensional data via

    • feature screening;

    • focus on important variables;

  2. large-sample data via

    • golden-section searching;

    • early-stop scheme;

  3. sparse inputs via

    • sparse matrix computation;

  4. specific models via

    • covariance update for LinearRegression and MultiTaskRegression;

    • quasi Newton iteration for LogisticRegression, PoissonRegression, CoxRegression, etc.

More importantly, the technique in these tips can be use simultaneously. For example, abess allow algorithms to use both feature screening and golden-section searching such that algorithms can handle datasets with large-sample and ultra-high dimension. The following contents illustrate the above tips.

Besides, abess efficiently implements warm-start initialization and parallel computing, which are very useful for fast computing. To help use leverage them, we will also describe their implementation details in the following.

Ultra-High dimensional data

Ultra-High dimensional data

Ultra-High dimensional data
Large-Sample Data

Large-Sample Data

Large-Sample Data
Sparse Inputs

Sparse Inputs

Sparse Inputs
Specific Models

Specific Models

Specific Models