L1-Regularized Optimization Algorithm

An L1-Regularized Optimization Algorithm is an regularized optimization algorithm that optimizes an L1-norm.

• AKA: ℓ1 Regularization.
• Context:
  • It can be solved by an L1-Regularized Optimization System (that can solve an L1-regularized optimization task).
• Example(s):
  • L1 Regularized Logistic Regression.
  • a Lasso Algorithm.
  • …
• Counter-Example(s):
  • L2-Regularized Optimization Algorithm.
  • L0-Regularized Optimization Algorithm.
• See: L1-Norm Regularizer, Regularization.

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References

2017

• https://towardsdatascience.com/l1-and-l2-regularization-methods-ce25e7fc831c
  • QUOTE: ... A regression model that uses L1 regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression. The key difference between these two is the penalty term. …

2007

• (Schmidt, Fung & Rosales, 2007) ⇒ Mark Schmidt, Glenn Fung, Romer Rosales. (2007). “Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches.” In: Proceedings of the 18th European conference on Machine Learning (ECML 2007). doi:10.1007/978-3-540-74958-5_28

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