Shaving Logs via Large Sieve Inequality: Faster Algorithms for Sparse Convolution and More

Jin, Ce; Xu, Yinzhan

doi:10.1145/3618260.3649605

Proceedings of the 56th Annual ACM Symposium on Theory of Computing 2024

DOI: 10.1145/3618260.3649605

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Shaving Logs via Large Sieve Inequality: Faster Algorithms for Sparse Convolution and More

Ce Jin,

Yinzhan Xu

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2024

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DPconv: Super-Polynomially Faster Join Ordering

Stoian,

Kipf

2024

Proc. ACM Manag. Data

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We revisit the join ordering problem in query optimization. The standard exact algorithm, DPccp, has a worst-case running time of O(3 n ). This is prohibitively expensive for large queries, which are not that uncommon anymore. We develop a new algorithmic framework based on subset convolution. DPconv achieves a super-polynomial speedup over DPccp, breaking the O(3 n ) time-barrier for the first time. We show that the framework instantiation for the C max cost function is up to 30x faster than DPccp for large clique queries.

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DPconv: Super-Polynomially Faster Join Ordering

Stoian,

Kipf

2024

Proc. ACM Manag. Data

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Shaving Logs via Large Sieve Inequality: Faster Algorithms for Sparse Convolution and More

Cited by 1 publication

References 55 publications

DPconv: Super-Polynomially Faster Join Ordering

DPconv: Super-Polynomially Faster Join Ordering

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