November 9, 2025Open Access

Randomized Rounding over Dynamic Programs

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Authors

ÉBÉtienne BamasÉcole Polytechnique Fédérale de Lausanne SLShi LiNanjing University of Science and Technology LRLars RohwedderUniversity of Southern Denmark

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Implication

Analysis reveals improved packing constraint approximations using randomized rounding in dynamic programming, suggesting broader applications in approximation algorithms.

Key Points

Randomized rounding enhances solutions in dynamic programming, showing promising results for approximation algorithms.
Approximation guarantees yield a polynomial time complexity of n^ε for packing constraints, broadening algorithm applicability.
Observational analysis across various dynamic programming problems reveals new potential for approximation algorithms in covering constraints.
Results indicate strong implications for incorporating additional constraints within dynamic programming, highlighting innovative connections to related problems.

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Bamas et al. (2025) studied this question.

synapsesocial.com/papers/690fdcdaf60c54d04ea381f3 https://doi.org/10.48550/arxiv.2511.03490

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Implication

Analysis reveals improved packing constraint approximations using randomized rounding in dynamic programming, suggesting broader applications in approximation algorithms.

Randomized Rounding over Dynamic Programs

Discussion

Member takes

Implication

Key Points

Cite This Study

Randomized Rounding over Dynamic Programs

Discussion

Member takes

Implication

Key Points

Cite This Study

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