{"title":"二部二项堆","authors":"Amr Elmasry, Claus Jensen, J. Katajainen","doi":"10.1051/ita/2017010","DOIUrl":null,"url":null,"abstract":"We describe a heap data structure that supports Minimum, Insert, and Borrow at O (1) worst-case cost, Delete at O (lg n ) worst-case cost including at most lg n + O (1) element comparisons, and Union at O (lg n ) worst-case cost including at most lg n + O (lglg n ) element comparisons, where n denotes the (total) number of elements stored in the data structure(s) prior to the operation. As the resulting data structure consists of two components that are different variants of binomial heaps, we call it a bipartite binomial heap. Compared to its counterpart, a multipartite binomial heap, the new structure is simpler and mergeable, still retaining the efficiency of the other operations.","PeriodicalId":438841,"journal":{"name":"RAIRO Theor. Informatics Appl.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Bipartite binomial heaps\",\"authors\":\"Amr Elmasry, Claus Jensen, J. Katajainen\",\"doi\":\"10.1051/ita/2017010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe a heap data structure that supports Minimum, Insert, and Borrow at O (1) worst-case cost, Delete at O (lg n ) worst-case cost including at most lg n + O (1) element comparisons, and Union at O (lg n ) worst-case cost including at most lg n + O (lglg n ) element comparisons, where n denotes the (total) number of elements stored in the data structure(s) prior to the operation. As the resulting data structure consists of two components that are different variants of binomial heaps, we call it a bipartite binomial heap. Compared to its counterpart, a multipartite binomial heap, the new structure is simpler and mergeable, still retaining the efficiency of the other operations.\",\"PeriodicalId\":438841,\"journal\":{\"name\":\"RAIRO Theor. Informatics Appl.\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"RAIRO Theor. Informatics Appl.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1051/ita/2017010\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"RAIRO Theor. Informatics Appl.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1051/ita/2017010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
摘要
我们描述了一个堆数据结构,它支持O(1)最坏情况下的最小值、插入和借用,O (lgn)最坏情况下的删除成本,包括最多lg n + O(1)个元素比较,以及O (lgn)最坏情况下的并集成本,包括最多lg n + O (lglg n)个元素比较,其中n表示在操作之前存储在数据结构中的元素总数。由于生成的数据结构由两部分组成,它们是二项堆的不同变体,因此我们称之为二部二项堆。与多部二项堆相比,新结构更简单,可合并,同时保留了其他操作的效率。
We describe a heap data structure that supports Minimum, Insert, and Borrow at O (1) worst-case cost, Delete at O (lg n ) worst-case cost including at most lg n + O (1) element comparisons, and Union at O (lg n ) worst-case cost including at most lg n + O (lglg n ) element comparisons, where n denotes the (total) number of elements stored in the data structure(s) prior to the operation. As the resulting data structure consists of two components that are different variants of binomial heaps, we call it a bipartite binomial heap. Compared to its counterpart, a multipartite binomial heap, the new structure is simpler and mergeable, still retaining the efficiency of the other operations.