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Robert Bredereck ;Jiehua Chen ;Piotr Faliszewski ;Jiong Guo ;Rolf Niedermeier ;Gerhard J.Woeginger 《清华大学学报》2014,(4):358-373
Computational Social Choice is an interdisciplinary research area involving Economics, Political Science,and Social Science on the one side, and Mathematics and Computer Science(including Artificial Intelligence and Multiagent Systems) on the other side. Typical computational problems studied in this field include the vulnerability of voting procedures against attacks, or preference aggregation in multi-agent systems. Parameterized Algorithmics is a subfield of Theoretical Computer Science seeking to exploit meaningful problem-specific parameters in order to identify tractable special cases of in general computationally hard problems. In this paper, we propose nine of our favorite research challenges concerning the parameterized complexity of problems appearing in this context. This work is dedicated to Jianer Chen, one of the strongest problem solvers in the history of parameterized algorithmics,on the occasion of his 60 th birthday. 相似文献
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将拟物方法与邻域搜索过程结合,得到求解不等圆Packing问题的拟物型邻域搜索算法(QP—NS).拟物方法用于连续优化,可从任一初始格局收敛至对应的局部最优格局;邻域搜索过程迭代地将当前格局替换为其邻域中的最优格局,直至无法继续改进当前格局为止.QP—NS可在不严重破坏当前格局的前提下稳定地改进当前格局,鲁棒性较强.基于14个国际公开算例的计算实验表明:QP-NS可在60S内改进10个算例的此前最优解,并与其余4个算例的此前最优解持平. 相似文献
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