Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Łącki, Jakub"'
The SetCover problem has been extensively studied in many different models of computation, including parallel and distributed settings. From an approximation point of view, there are two standard guarantees: an $O(\log \Delta)$-approximation (where $
Externí odkaz:
http://arxiv.org/abs/2408.13362
Autor:
Azarmehr, Amir, Behnezhad, Soheil, Jayaram, Rajesh, Łącki, Jakub, Mirrokni, Vahab, Zhong, Peilin
We study the minimum spanning tree (MST) problem in the massively parallel computation (MPC) model. Our focus is particularly on the *strictly sublinear* regime of MPC where the space per machine is $O(n^\delta)$. Here $n$ is the number of vertices a
Externí odkaz:
http://arxiv.org/abs/2408.06455
Autor:
Bateni, MohammadHossein, Dhulipala, Laxman, Fletcher, Willem, Gowda, Kishen N, Hershkowitz, D Ellis, Jayaram, Rajesh, Łącki, Jakub
We give an efficient algorithm for Centroid-Linkage Hierarchical Agglomerative Clustering (HAC), which computes a $c$-approximate clustering in roughly $n^{1+O(1/c^2)}$ time. We obtain our result by combining a new Centroid-Linkage HAC algorithm with
Externí odkaz:
http://arxiv.org/abs/2406.05066
Autor:
Wheatman, Brian, Dong, Xiaojun, Shen, Zheqi, Dhulipala, Laxman, Łącki, Jakub, Pandey, Prashant, Xu, Helen
A fundamental building block in any graph algorithm is a graph container - a data structure used to represent the graph. Ideally, a graph container enables efficient access to the underlying graph, has low space usage, and supports updating the graph
Externí odkaz:
http://arxiv.org/abs/2405.11671
We consider the PageRank problem in the dynamic setting, where the goal is to explicitly maintain an approximate PageRank vector $\pi \in \mathbb{R}^n$ for a graph under a sequence of edge insertions and deletions. Our main result is a complete chara
Externí odkaz:
http://arxiv.org/abs/2404.16267
Autor:
Bateni, MohammadHossein, Dhulipala, Laxman, Gowda, Kishen N, Hershkowitz, D Ellis, Jayaram, Rajesh, Łącki, Jakub
Average linkage Hierarchical Agglomerative Clustering (HAC) is an extensively studied and applied method for hierarchical clustering. Recent applications to massive datasets have driven significant interest in near-linear-time and efficient parallel
Externí odkaz:
http://arxiv.org/abs/2404.14730
We consider the fundamental problem of decomposing a large-scale approximate nearest neighbor search (ANNS) problem into smaller sub-problems. The goal is to partition the input points into neighborhood-preserving shards, so that the nearest neighbor
Externí odkaz:
http://arxiv.org/abs/2403.01797
Autor:
Ajwani, Deepak, Bisseling, Rob H., Casel, Katrin, Çatalyürek, Ümit V., Chevalier, Cédric, Chudigiewitsch, Florian, Faraj, Marcelo Fonseca, Fellows, Michael, Gottesbüren, Lars, Heuer, Tobias, Karypis, George, Kaya, Kamer, Lacki, Jakub, Langguth, Johannes, Li, Xiaoye Sherry, Mayer, Ruben, Meintrup, Johannes, Mizutani, Yosuke, Pellegrini, François, Petrini, Fabrizio, Rosamond, Frances, Safro, Ilya, Schlag, Sebastian, Schulz, Christian, Sharma, Roohani, Strash, Darren, Sullivan, Blair D., Uçar, Bora, Yzelman, Albert-Jan
Large networks are useful in a wide range of applications. Sometimes problem instances are composed of billions of entities. Decomposing and analyzing these structures helps us gain new insights about our surroundings. Even if the final application c
Externí odkaz:
http://arxiv.org/abs/2310.11812
We introduce TeraHAC, a $(1+\epsilon)$-approximate hierarchical agglomerative clustering (HAC) algorithm which scales to trillion-edge graphs. Our algorithm is based on a new approach to computing $(1+\epsilon)$-approximate HAC, which is a novel comb
Externí odkaz:
http://arxiv.org/abs/2308.03578
We study the consistent k-center clustering problem. In this problem, the goal is to maintain a constant factor approximate $k$-center solution during a sequence of $n$ point insertions and deletions while minimizing the recourse, i.e., the number of
Externí odkaz:
http://arxiv.org/abs/2307.13747