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Least absolute deviations with L1 regularization using majorization-minimization

Project description

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Least absolute deviations with L1 regularization using majorization-minimization. In estimation theory terms, this is the Maximum A Posterior (MAP) estimator for a Laplacian likelihood with Laplacian prior, i.e.


The MM algorithm update turns out to be


which belongs to the class of iterative reweighted least-squares:

Python Version

To install the development version, proceed as follows:

git clone
pip install -e lad

Or install the lastest version on PyPi:

pip install lad

Installation dependencies:

- tensorflow

Test dependencies:

- numpy
- tensorflow
- pytest
- pytest-cov

R version

Inside the R console, type:


Project details

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Source Distribution

lad-0.1.dev4.tar.gz (3.1 kB view hashes)

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