The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years. Non-cooperative targets exhibit uncertain states and unpredictable behaviors, making ...
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The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years. Non-cooperative targets exhibit uncertain states and unpredictable behaviors, making collision avoidance significantly more challenging than that for space debris. Much existing research focuses on the continuous thrust model, whereas the impulsive maneuver model is more appropriate for long-duration and long-distance avoidance missions. Additionally, it is important to minimize the impact on the original mission while avoiding noncooperative targets. On the other hand, the existing avoidance algorithms are computationally complex and time-consuming especially with the limited computing capability of the on-board computer, posing challenges for practical engineering applications. To conquer these difficulties, this paper makes the following key contributions: (A) a turn-based (sequential decision-making) limited-area impulsive collision avoidance model considering the time delay of precision orbit determination is established for the first time;(B) a novel Selection Probability Learning Adaptive search-depth searchtree (SPL-ASST) algorithm is proposed for non-cooperative target avoidance, which improves the decision-making efficiency by introducing an adaptive-search-depth mechanism and a neural network into the traditional Monte Carlo treesearch (MCTS). Numerical simulations confirm the effectiveness and efficiency of the proposed method. (c) 2024 Production and hosting by Elsevier Ltd. on behalf of Chinese Society of Aeronautics and Astronautics. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).
Given a collection C of partitions of a base set S, the NP-hard CONSENSUS CLUSTERING problem asks for a partition of S which has a total Mirkin distance of at most t to the partitions in C, where t is a nonnegative in...
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Given a collection C of partitions of a base set S, the NP-hard CONSENSUS CLUSTERING problem asks for a partition of S which has a total Mirkin distance of at most t to the partitions in C, where t is a nonnegative integer. We present a parameterized algorithm for CONSENSUS CLUSTERING with running time O (4.24(k) . k(3) + vertical bar C vertical bar . vertical bar S vertical bar(2)), where k := t/vertical bar C vertical bar is the average Mirkin distance of the solution partition to the partitions of C. Furthermore, we strengthen previous hardness results for CONSENSUS CLUSTERING, showing that CONSENSUS CLUSTERING remains NP-hard even when all input partitions contain at most two subsets. Finally, we study a local search variant of CONSENSUS CLUSTERING, showing W[1]-hardness for the parameter "radius of the Mirkin-distance neighborhood". In the process, we also consider a local search variant of the related CLUSTER EDITING problem, showing W[1]-hardness for the parameter "radius of the edge modification neighborhood". (C) 2014 Published by Elsevier B.V.
Motivated by applications in the analysis of genetic networks, we introduce and study the NP-hard Module Map problem which has as input a graph G = (V, E) with red and blue edges and an integer k and asks to transform...
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Motivated by applications in the analysis of genetic networks, we introduce and study the NP-hard Module Map problem which has as input a graph G = (V, E) with red and blue edges and an integer k and asks to transform G by at most k edge modifications into a graph G' which has the following properties: the vertex set of G' can be partitioned into so-called clusters such that inside a cluster every pair of vertices is connected by a blue edge and for two distinct clusters A and B either all vertices u is an element of A and v is an element of B are connected by a red edge or there is no edge between A and B. We show that Module MAP can be solved in O(3(k) . (vertical bar V vertical bar + vertical bar E vertical bar)) time and O(2(k) . vertical bar V vertical bar(3)) time, respectively. Furthermore, we show that Module Map admits a kernel with O(k(2)) vertices. (C) 2020 Elsevier B.V. All rights reserved.
The Cluster Editing problem asks to transform a graph by at most k edge modifications into a disjoint union of cliques. The problem is NP-complete, but several parameterized algorithms are known. We present a novel se...
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The Cluster Editing problem asks to transform a graph by at most k edge modifications into a disjoint union of cliques. The problem is NP-complete, but several parameterized algorithms are known. We present a novel search tree algorithm for the problem, which improves running time from 0(1.76(k) + m + n) to 0(1.62(k) + m + n) for m edges and n vertices. In detail, we can show that we can always branch with branching vector (2, 1) or better, resulting in the golden ratio as the base of the searchtree size. Our algorithm uses a well-known transformation to the integer-weighted counterpart of the problem. To achieve our result, we combine three techniques: First, we show that zero-edges in the graph enforce structural features that allow us to branch more efficiently. This is achieved by keeping track of the parity of merged vertices. Second, by repeatedly branching we can isolate vertices, releasing cost. Third, we use a known characterization of graphs with few conflicts. We then show that Integer-Weighted Cluster Editing remains NP-hard for graphs that have a particularly simple structure: namely, a clique minus the edges of a triangle. (C) 2012 Elsevier B.V. All rights reserved.
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