Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Vasileios Nakos"'
Autor:
Karl Bringmann, Michael Kapralov, Mikhail Makarov, Vasileios Nakos, Amir Yagudin, Amir Zandieh
Publikováno v:
Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) ISBN: 9781611977554
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bc0638c0192243ebe3b235be3d40c256
https://doi.org/10.1137/1.9781611977554.ch177
https://doi.org/10.1137/1.9781611977554.ch177
Autor:
Baris Can Esmer, Vasileios Nakos
Publikováno v:
2022 IEEE International Symposium on Information Theory (ISIT).
Autor:
Vasileios Nakos
Publikováno v:
IEEE Transactions on Information Theory. 66:7231-7236
In the sparse polynomial multiplication problem, one is asked to multiply two sparse polynomials f and g in time that is proportional to the size of the input plus the size of the output. The polynomials are given via lists of their coefficients F an
Publikováno v:
STOC '22: 54th Annual ACM SIGACT Symposium on Theory of Computing
STOC '22
Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing
STOC '22
Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing
We revisit the task of computing the edit distance in sublinear time. In the $(k,K)$-gap edit distance problem the task is to distinguish whether the edit distance of two strings is at most $k$ or at least $K$. It has been established by Goldenberg,
Autor:
Vasileios Nakos, Arturs Backurs, Hongxun Wu, Karl Bringmann, Ce Jin, Kyriakos Axiotis, Christos Tzamos
Publikováno v:
SOSA
We revisit the Subset Sum problem over the finite cyclic group $\mathbb{Z}_m$ for some given integer $m$. A series of recent works has provided near-optimal algorithms for this problem under the Strong Exponential Time Hypothesis. Koiliaris and Xu (S
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::514cd34769df4216b27fc749a892ea2c
https://doi.org/10.1137/1.9781611976496.6
https://doi.org/10.1137/1.9781611976496.6
Publikováno v:
STOC '21
STOC
STOC
Computing the convolution $A\star B$ of two length-$n$ vectors $A,B$ is an ubiquitous computational primitive. Applications range from string problems to Knapsack-type problems, and from 3SUM to All-Pairs Shortest Paths. These applications often come
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a412f9a5f9300a3fabf3896a0d40394
https://hdl.handle.net/21.11116/0000-0008-E23F-321.11116/0000-0008-E241-F
https://hdl.handle.net/21.11116/0000-0008-E23F-321.11116/0000-0008-E241-F
Autor:
Karl Bringmann, Vasileios Nakos
Publikováno v:
SODA
Approximating Subset Sum is a classic and fundamental problem in computer science and mathematical optimization. The state-of-the-art approximation scheme for Subset Sum computes a $(1-\varepsilon)$-approximation in time $\tilde{O}(\min\{n/\varepsilo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c0d6a693aece16c9f082e9a0df31a1e1
https://doi.org/10.1137/1.9781611976465.108
https://doi.org/10.1137/1.9781611976465.108
Autor:
Kasper Green Larsen, Jaroslaw Blasiok, Michael Mitzenmacher, Preetum Nakkiran, Ben Lawson, Charalampos E. Tsourakakis, Vasileios Nakos
Publikováno v:
Internet Mathematics.
Social networks involve both positive and negative relationships, which can be captured in signed graphs. The {\em edge sign prediction problem} aims to predict whether an interaction between a pair of nodes will be positive or negative. We provide t
Autor:
Karl Bringmann, Vasileios Nakos
Publikováno v:
STOC
In the classical SubsetSum problem we are given a set X and a target t, and the task is to decide whether there exists a subset of X which sums to t. A recent line of research has resulted in (t · poly (logt))-time algorithms, which are (near-)optim
Autor:
Vasileios Nakos
Publikováno v:
ISIT
Is it possible to obliviously construct a set of hyperplanes $\mathcal{H}$, such that you can approximate a unit vector x when you are given the side on which the vector lies with respect to every ${\text{h}} \in \mathcal{H}$ƒ In the sparse recovery