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| Content Provider | ACM Digital Library |
|---|---|
| Author | Williams, Ryan Impagliazzo, Russell Kolokolova, Antonina Gao, Jiawei |
| Abstract | Properties definable in first-order logic are algorithmically interesting for both theoretical and pragmatic reasons. Many of the most studied algorithmic problems, such as Hitting Set and Orthogonal Vectors, are first-order, and the first-order properties naturally arise as relational database queries. A relatively straightforward algorithm for evaluating a property with k + 1 quantifiers takes time $O(m^{k})$ and, assuming the Strong Exponential Time Hypothesis (SETH), some such properties require $O(m^{k™ϵ})$ time for any ϵ > 0. (Here, m represents the size of the input structure, i.e. the number of tuples in all relations.) We give algorithms for every first-order property that improves this upper bound to [EQUATION], i.e., an improvement by a factor more than any poly-log, but less than the polynomial required to refute SETH. Moreover, we show that further improvement is equivalent to improving algorithms for sparse instances of the well-studied Orthogonal Vectors problem. Surprisingly, both results are obtained by showing completeness of the Sparse Orthogonal Vectors problem for the class of first-order properties under fine-grained reductions. To obtain improved algorithms, we apply the fast Orthogonal Vectors algorithm of [3, 16]. While fine-grained reductions (reductions that closely preserve the conjectured complexities of problems) have been used to relate the hardness of disparate specific problems both within P and beyond, this is the first such completeness result for a standard complexity class. |
| Starting Page | 2162 |
| Ending Page | 2181 |
| Page Count | 20 |
| File Format | |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-01-16 |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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