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Titlebook: Structural Information and Communication Complexity; 28th International C Tomasz Jurdziński,Stefan Schmid Conference proceedings 2021 Sprin

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樓主: Destruct
41#
發(fā)表于 2025-3-28 16:22:38 | 只看該作者
42#
發(fā)表于 2025-3-28 20:11:54 | 只看該作者
Collecting Coupons is Faster with Friends the problem. While our analysis is in most cases asymptotically tight, there are several open questions raised, regarding finer-grained analysis of both “coupon collecting with friends,” and of a long-studied variant of the original problem in which a collector requires multiple full sets of coupon
43#
發(fā)表于 2025-3-29 01:16:26 | 只看該作者
44#
發(fā)表于 2025-3-29 03:08:42 | 只看該作者
45#
發(fā)表于 2025-3-29 08:59:55 | 只看該作者
Near-Optimal Scheduling in the Congested Cliquelgorithm to the previous approaches and show their benefit..We schedule the set of jobs on-the-fly, without a priori knowledge of its parameters or the communication patterns of the jobs. In light of the inherent lower bounds, all of our algorithms are nearly-optimal..We exemplify the power of our a
46#
發(fā)表于 2025-3-29 15:23:03 | 只看該作者
47#
發(fā)表于 2025-3-29 19:25:59 | 只看該作者
Threshold-Based Network Structural Dynamicsmeaningful microscopic local rules that give rise to interesting macroscopic behaviors. Our goals are the following: a) to investigate the properties of the .-Thresholded Network Dynamics and b) to show that .-Dynamics is expressive enough to solve complex problems on networks..Our contribution in t
48#
發(fā)表于 2025-3-29 23:15:47 | 只看該作者
New Approximation Algorithms for the Heterogeneous Weighted Delivery Problem polynomial-time 8-approximation algorithm for ., closing a problem left open in [B?rtschi et al., ATMOS’17]. This algorithm can be turned into a .(.)-approximation algorithm that always runs in polynomial-time, regardless of the values of .. Then, we show that HWD problem is 36-approximable in poly
49#
發(fā)表于 2025-3-30 03:54:46 | 只看該作者
50#
發(fā)表于 2025-3-30 07:29:14 | 只看該作者
Pebble Guided Near Optimal Treasure Hunt in Anonymous Graphshunt algorithm regardless of the number of pebbles are placed?.We show an algorithm that uses . pebbles to find the treasure in a graph . in time ., where . is the maximum degree of a node in . and . is the distance from the initial position of the agent to the treasure. We show an almost matching l
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