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A generalized lasso formulation of weighted and regularized (nearly-)isotonic regression on graphs — arXiv:2611.04018

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04018
License (as archived): CC0-1.0
Archived: 2026-11-04T18:58:01.448Z
SHA-256 of the archived text: dc0572298464a617e57693a5e39b50dc5bf8cc1ef7db2888f8c43c51a893183f
Stable URL of this page: https://metraipolis.com/documento/dc_aa99efaea8a0

How to cite

A generalized lasso formulation of weighted and regularized (nearly-)isotonic regression on graphs — arXiv:2611.04018 — arXiv (en), https://arxiv.org/abs/2611.04018 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_aa99efaea8a0 (Metraipolis archive dc_aa99efaea8a0, sha256 dc0572298464a617e57693a5e39b50dc5bf8cc1ef7db2888f8c43c51a893183f)

BibTeX

@misc{dcaa99efaea8a0,
  title = {A generalized lasso formulation of weighted and regularized (nearly-)isotonic regression on graphs — arXiv:2611.04018},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04018},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_aa99efaea8a0; sha256 dc0572298464a617e57693a5e39b50dc5bf8cc1ef7db2888f8c43c51a893183f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04018. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In this paper we study a regularized version of nearly-isotonic regression with respect to a general partial order. In this estimator the sum of squares is weighted, with some of the weights allowed to be zero, and an additional quadratic penalty is imposed. The only assumption we make is that the quadratic part of the objective function is strictly convex, and we show that this assumption is mild. First, we show that for all sufficiently large values of the penalty parameter the estimator coincides with the regularized isotonic regression, in which the penalty is replaced by the isotonic constraint. Further, we prove that the resulting problem can be written as a generalized lasso problem with a design matrix of full column rank and with the penalty matrix unchanged. Next, we derive the dual problem, which is a quadratic program with box constraints, and write it in a sparse form that can be solved by standard quadratic programming solvers for sparse problems.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ME, nuovi lavori del giorno).
Categorie arXiv: Methodology (stat.ME) ; Other Statistics (stat.OT).
Pagina del lavoro: https://arxiv.org/abs/2611.04018 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T18:36:37.143Z dalla pagina https://arxiv.org/list/stat.ME/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).