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This document summarizes an optimization approach called Least Squares Optimization with L1-Norm Regularization. It first reviews linear regression, least squares optimization, and L2 regularization. It then discusses the motivation and problem formulation for optimizing least squares with an L1 penalty on the regression coefficients. The bulk of the document surveys 8 different optimization strategies proposed for this problem, examining their strengths, weaknesses, and how they handle non-differentiable p

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