Approximate Set Union Via Approximate Randomization

February 17, 2018 Β· Declared Dead Β· πŸ› International Computing and Combinatorics Conference

πŸ‘» CAUSE OF DEATH: Ghosted
No code link whatsoever

"No code URL or promise found in abstract"

Evidence collected by the PWNC Scanner

Authors Bin Fu, Pengfei Gu, Yuming Zhao arXiv ID 1802.06204 Category cs.DS: Data Structures & Algorithms Cross-listed cs.CC, cs.CG, cs.DM Citations 0 Venue International Computing and Combinatorics Conference Last Checked 5 months ago
Abstract
We develop an randomized approximation algorithm for the size of set union problem $\arrowvert A_1\cup A_2\cup...\cup A_m\arrowvert$, which given a list of sets $A_1,...,A_m$ with approximate set size $m_i$ for $A_i$ with $m_i\in \left((1-Ξ²_L)|A_i|, (1+Ξ²_R)|A_i|\right)$, and biased random generators with $Prob(x=\randomElm(A_i))\in \left[{1-Ξ±_L\over |A_i|},{1+Ξ±_R\over |A_i|}\right]$ for each input set $A_i$ and element $x\in A_i,$ where $i=1, 2, ..., m$. The approximation ratio for $\arrowvert A_1\cup A_2\cup...\cup A_m\arrowvert$ is in the range $[(1-Ξ΅)(1-Ξ±_L)(1-Ξ²_L), (1+Ξ΅)(1+Ξ±_R)(1+Ξ²_R)]$ for any $Ξ΅\in (0,1)$, where $Ξ±_L, Ξ±_R, Ξ²_L,Ξ²_R\in (0,1)$. The complexity of the algorithm is measured by both time complexity, and round complexity. The algorithm is allowed to make multiple membership queries and get random elements from the input sets in one round. Our algorithm makes adaptive accesses to input sets with multiple rounds. Our algorithm gives an approximation scheme with $O(\setCount\cdot(\log \setCount)^{O(1)})$ running time and $O(\log m)$ rounds, where $m$ is the number of sets. Our algorithm can handle input sets that can generate random elements with bias, and its approximation ratio depends on the bias. Our algorithm gives a flexible tradeoff with time complexity $O\left(\setCount^{1+ΞΎ}\right)$ and round complexity $O\left({1\over ΞΎ}\right)$ for any $ΞΎ\in(0,1)$.
Community shame:
Not yet rated
Community Contributions

Found the code? Know the venue? Think something is wrong? Let us know!

πŸ“œ Similar Papers

In the same crypt β€” Data Structures & Algorithms

Died the same way β€” πŸ‘» Ghosted