CLASSO: a practical primal path-following algorithm for the constrained LASSO

Key points

• The local direction is obtained from a quadratic programme on an augmented active set, which determines both the coefficients that leave zero and the weakly active inequalities that remain binding.1
• We prove that the primal path is piecewise affine with finitely many kinks, without a constraint qualification or unique multipliers.1
• We assess the implementation on a $39$-case synthetic suite and six constrained versions of the diabetes benchmark.1
• On the $31$ nontrivial synthetic cases with $n\ge p$, sampled relative objective differences from numerical reference solves are below $10^{-9}$ in magnitude; unnormalised endpoint differences reach $1.5\times10^{-7}$ in magnitude.1

Sources (1)

• [1]CLASSO: a practical primal path-following algorithm for the constrained LASSO
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 04:02 PM

  We present CLASSO, a path-following method for the LASSO subject to general linear equality and inequality constraints.

  The local direction is obtained from a quadratic programme on an augmented active set, which determines both the coefficients that leave zero and the weakly active inequalities that remain binding.

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