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New algorithms tackle precedence-constrained decision trees and set cover

Researchers have developed new approximation algorithms for optimization problems involving precedence constraints, specifically focusing on the Optimal Decision Tree and Set Cover problems. The work introduces algorithmic reductions that allow an approximation algorithm for one problem to be used for another. A key contribution is the Maximum Density Precedence-Closed Subfamily problem, which captures the combinatorial essence of the main problems. The study provides polynomial-time algorithms with $\mathcal{O}^*(\sqrt{m})$-approximation guarantees and establishes hardness results indicating $\mathcal{O}(m^{1/12-\epsilon})$-inapproximability for these problems. AI

IMPACT Introduces theoretical advancements in algorithmic optimization relevant to machine learning model design.

RANK_REASON Academic paper detailing new algorithms and theoretical results for optimization problems. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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New algorithms tackle precedence-constrained decision trees and set cover

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Academic paper detailing new algorithms and theoretical results for optimization problems. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Micha{\l} Szyfelbein, Dariusz Dereniowski ·

    Precedence-Constrained Decision Trees and Coverings

    arXiv:2602.21312v4 Announce Type: replace-cross Abstract: This work considers a number of optimization problems and reductive relations between them. The two main problems we are interested in are the Optimal Decision Tree and Set Cover. We study these two fundamental tasks under…