Interval Selection in Sliding Windows
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913576143290368 |
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| author | Alexandru, Cezar-Mihail Konrad, Christian |
| author_facet | Alexandru, Cezar-Mihail Konrad, Christian |
| contents | We initiate the study of the Interval Selection problem in the (streaming) sliding window model of computation.
In this problem, an algorithm receives a potentially infinite stream of intervals on the line, and the objective is to maintain at every moment an approximation to a largest possible subset of disjoint intervals among the $L$ most recent intervals, for some integer $L$.
We give the following results:
- In the unit-length intervals case, we give a $2$-approximation sliding window algorithm with space $\tilde{\mathrm{O}}(|OPT|)$, and we show that any sliding window algorithm that computes a $(2-\varepsilon)$-approximation requires space $Ω(L)$, for any $\varepsilon > 0$.
- In the arbitrary-length case, we give a $(\frac{11}{3}+\varepsilon)$-approximation sliding window algorithm with space $\tilde{\mathrm{O}}(|OPT|)$, for any constant $\varepsilon > 0$, which constitutes our main result.
We also show that space $Ω(L)$ is needed for algorithms that compute a $(2.5-\varepsilon)$-approximation, for any $\varepsilon > 0$.
Our main technical contribution is an improvement over the smooth histogram technique, which consists of running independent copies of a traditional streaming algorithm with different start times. By employing the one-pass $2$-approximation streaming algorithm by Cabello and Pérez-Lantero [Theor. Comput. Sci. '17] for Interval Selection on arbitrary-length intervals as the underlying algorithm, the smooth histogram technique immediately yields a $(4+\varepsilon)$-approximation in this setting. Our improvement is obtained by forwarding the structure of the intervals identified in a run to the subsequent run, which constrains the shape of an optimal solution and allows us to target optimal intervals differently. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09338 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Interval Selection in Sliding Windows Alexandru, Cezar-Mihail Konrad, Christian Data Structures and Algorithms We initiate the study of the Interval Selection problem in the (streaming) sliding window model of computation. In this problem, an algorithm receives a potentially infinite stream of intervals on the line, and the objective is to maintain at every moment an approximation to a largest possible subset of disjoint intervals among the $L$ most recent intervals, for some integer $L$. We give the following results: - In the unit-length intervals case, we give a $2$-approximation sliding window algorithm with space $\tilde{\mathrm{O}}(|OPT|)$, and we show that any sliding window algorithm that computes a $(2-\varepsilon)$-approximation requires space $Ω(L)$, for any $\varepsilon > 0$. - In the arbitrary-length case, we give a $(\frac{11}{3}+\varepsilon)$-approximation sliding window algorithm with space $\tilde{\mathrm{O}}(|OPT|)$, for any constant $\varepsilon > 0$, which constitutes our main result. We also show that space $Ω(L)$ is needed for algorithms that compute a $(2.5-\varepsilon)$-approximation, for any $\varepsilon > 0$. Our main technical contribution is an improvement over the smooth histogram technique, which consists of running independent copies of a traditional streaming algorithm with different start times. By employing the one-pass $2$-approximation streaming algorithm by Cabello and Pérez-Lantero [Theor. Comput. Sci. '17] for Interval Selection on arbitrary-length intervals as the underlying algorithm, the smooth histogram technique immediately yields a $(4+\varepsilon)$-approximation in this setting. Our improvement is obtained by forwarding the structure of the intervals identified in a run to the subsequent run, which constrains the shape of an optimal solution and allows us to target optimal intervals differently. |
| title | Interval Selection in Sliding Windows |
| topic | Data Structures and Algorithms |
| url | https://arxiv.org/abs/2405.09338 |