有向基因组移位排序问题的O(n2)快速算法

有向基因组移位排序问题在计算生物学研究中占有重要位置．以前最好的算法时间复杂度为O(n^2logn)．该文给出一个有向基因组移位排序的新多项式算法，将移位排序的时间复杂度改进为O(n^2)．算法改进的关键在于找到一种寻找有效合理移位的新方法，通过在最小子排列中删除无关顶点确定一个合理移位是否有效，从而将寻找一个有效移位的时间复杂度改进为O(n)，总时间复杂度由此降为O(n^2)．     

链表递增归并排序算法

归并排序是一种稳定．高效的排序算法。归并排序算法一般是用顺序存储结构实现的。如Sun公司JDK中Java Collection库中对数组、List的排序。使用顺序存储结构实现归并排序需要空间复杂度为O(n)的辅助存储空间，对于链表来说，还需要转换为顺序存储结构，所以共需要2n的辅助存储空间。本文提出一种链表非递归归并排序算法，可以对链表进行原地(In Place)排序，只需要O(logn)辅助存储空间，时间复杂度不变。     

链表递增归并排序算法

归并排序是一种稳定,高效的排序算法。归并排序算法一般是用顺序存储结构实现的。如Sun公司JDK中Java Collection库中对数组、List的排序。使用顺序存储结构实现归并排序需要空间复杂度为O(n)的辅助存储空间,对于链表来说,还需要转换为顺序存储结构,所以共需要2n的辅助存储空间。本文提出一种链表非递归归并排序算法,可以对链表进行原地(In Place)排序,只需要O(logn)的辅助存储空间,时间复杂度不变。     

基于立方体剖分的传感器网络快速三维k-覆盖判定算法

提出了一种传感器网络中基于立方体剖分的三维k 覆盖快速判定算（CP-RTCDA）和三维最大k 覆盖问题的快速求解算法（CP-RTMCDA）。算法首先把感兴趣区域剖分为立方体区域，从而将复杂的空间区域覆盖问题转化为简单的立方体区域覆盖问题。理论分析与仿真实验表明, 针对具有n个节点的传感器网络, 新算法的计算时间复杂度为O(n)，远低于已有算法O(n3logn)的计算时间复杂度。     

基于实时逻辑的时间约束检测方法

本文针对具有严格时间要求的系统,阐述并分析了三种利用实时逻辑实现时间约束检测的方法。第一种方法通过检测系统规范和安全性断言的一致性来验证约束的满足性,非常适合于系统规范的设计与可满足性检测,算法的时间复杂度是O(n^2) O(n^2) O(2^k)。第二种方法利用实时逻辑与约束图的方法实现运行时的时间约束检测,但检测时的系统约束条件不够第三种方法简约,算法时间复杂度为O(n^2),改进之后为O(n^2)。第三种方法通过对约束图的处理,减少运行时系统检测的约束条件,从而减少运行时的时间约束条件的搜索时间,算法的时间复杂度为O(n),在实时性和检测效率明显优于前两种方法。但需要运行前优化约束规则,将会增加额外的时间和空间复杂度。     

中值滤波的快速算法

利用矩形相邻窗口间的相关信息，提出了中值滤波的快速算法。该算法可将传统算法的复杂度O(n^2）简化为O(n），运算速度大大提高。     

基于凸片段分解和格网的点在多边形中的可见边检测

检测点在多边形中的可见边是计算几何中的一种基本计算,文中对此提出一种加速算法.首先对多边形进行凸片段分解,以利用点在凸多边形中可见边的快速计算;然后利用格网结构实现由近及远的计算,避免处理被遮挡的凸片段.该算法可基于格网结构方便地进行并行处理,并可统一处理含空洞和不含空洞的多边形,其预处理时间复杂度为O(n),空间复杂度也是很低的O(n),而检测的时间复杂度在O(logn)～O(n)之间自适应变化,其中n为多边形的边数.     

汉诺塔非递归算法研究

汉诺塔(Tower of Hanoi)问题是求在三个柱子之间移动圆盘的方法,它是递归程序设计的经典例子,已经证明其时间复杂度下限是O(2n),空间复杂度是O(n),实际使用时很容易溢出.给出汉诺塔问题的两个非递归算法:解集递推法和解集树法.解集递推法的时间复杂度和空间复杂度都是O(2n),该算法空间复杂度很大,无法实际使用,提出该算法的目的是为了引出解集树法.解集树法可以计算出指定的任意一步移动方法,时间复杂度和空间复杂度分别是O(n*2n)和O(1).并证明了汉诺塔问题的空间复杂度下限是O(1).     

位排序--一种快速排序法

众所周知,排序速度的快慢,取决于排序算法的时间复杂度和空间复杂度。因而,排序算法设计的主导思想,就是要千方百计降低算法的时间复杂度和空间复杂度。虽然计算机硬件的运算速度越来越快,但排序算法的研究仍是算法理论中的一个重要课题。已有的排序算法很多,在所有基于“记录关键字之间比较”的排序方法中,快速排序(quick sort)是平均时间性能最好的一种方法,平均时间为O（n*log n)。但是在最坏情况下,时间复杂度却很高,为O(n^2)。     

最近点问题的三角不等式算法

本文用树结构存贮有限空间的点.然后,设计了一个查找针对已知查询点的最近点的算法——三角不等式算法.整个算法的空间复杂性为O(n);预处理和查询时间复杂性分别为O(n·logn)和O(c·logn), c<相似文献   

Efficient EREW PRAM algorithms for parentheses-matching

We present four polylog-time parallel algorithms for matching parentheses on an exclusive-read and exclusive-write (EREW) parallel random-access machine (PRAM) model. These algorithms provide new insights into the parentheses-matching problem. The first algorithm has a time complexity of O(log² n) employing O(n/(log n)) processors for an input string containing n parentheses. Although this algorithm is not cost-optimal, it is extremely simple to implement. The remaining three algorithms, which are based on a different approach, achieve O(log n) time complexity in each case, and represent successive improvements. The second algorithm requires O(n) processors and working space, and it is comparable to the first algorithm in its ease of implementation. The third algorithm uses O(n/(log n)) processors and O(n log n) space. Thus, it is cost-optimal, but uses extra space compared to the standard stack-based sequential algorithm. The last algorithm reduces the space complexity to O(n) while maintaining the same processor and time complexities. Compared to other existing time-optimal algorithms for the parentheses-matching problem that either employ extensive pipelining or use linked lists and comparable data structures, and employ sorting or a linked list ranking algorithm as subroutines, the last two algorithms have two distinct advantages. First, these algorithms employ arrays as their basic data structures, and second, they do not use any pipelining, sorting, or linked list ranking algorithms     

An Optimal Parallel Co-Connectivity Algorithm

In this paper we consider the problem of computing the connected components of the complement of a given graph. We describe a simple sequential algorithm for this problem, which works on the input graph and not on its complement, and which for a graph on n vertices and m edges runs in optimal O(n+m) time. Moreover, unlike previous linear co-connectivity algorithms, this algorithm admits efficient parallelization, leading to an optimal O(log n)-time and O((n+m)log n)-processor algorithm on the EREW PRAM model of computation. It is worth noting that, for the related problem of computing the connected components of a graph, no optimal deterministic parallel algorithm is currently available. The co-connectivity algorithms find applications in a number of problems. In fact, we also include a parallel recognition algorithm for weakly triangulated graphs, which takes advantage of the parallel co-connectivity algorithm and achieves an O(log² n) time complexity using O((n+m²) log n) processors on the EREW PRAM model of computation.     

Deterministic Resource Discovery in Distributed Networks

The resource discovery problem was introduced by Harchol-Balter, Leighton, and Lewin. They developed a number of algorithms for the problem in the weakly connected directed graph model. This model is a directed logical graph that represents the vertices’ knowledge about the topology of the underlying communication network. The current paper proposes a deterministic algorithm for the problem in the same model, with improved time, message, and communication complexities. Each previous algorithm had a complexity that was higher at least in one of the measures. Specifically, previous deterministic solutions required either time linear in the diameter of the initial network, or communication complexity $O(n^3)$ (with message complexity $O(n^2)$), or message complexity $O(|E_0| \log n)$ (where $E_0$ is the arc set of the initial graph $G_0$). Compared with the main randomized algorithm of Harchol-Balter, Leighton, and Lewin, the time complexity is reduced from $O(\log^2n)$ to\pagebreak[4] $O(\log n )$, the message complexity from $O(n \log^2 n)$ to $O(n \log n )$, and the communication complexity from $O(n^2 \log^3 n)$ to $O(|E_0|\log ^2 n )$. \par Our work significantly extends the connectivity algorithm of Shiloach and Vishkin which was originally given for a parallel model of computation. Our result also confirms a conjecture of Harchol-Balter, Leighton, and Lewin, and addresses an open question due to Lipton.     

完全欧几里德距离变换的最优算法

欧几里德距离变换（ＥＤＴ）对由黑白素构成的二值图象中所有象素找出其到最近黑素的距离，应用于图象分析，计算机视觉，在本文之前，该问题的最好复杂度为Ｏ（ｎ＾２ｌｏｇｎ）。本文提出了一个复杂度为Ｏ（ｎ＾２）的算法，使复杂度达到最优，该算法可以并行化，在有ｒ个处理单元的ＥＲＥＷＰＲＡＭ计算模型上，若ｒｌｏｇｒ≤２２／６ｎ，则时间复杂度为Ｏ（ｎ／ｒ）否则为Ｏ（ｎｌｏｇｒ）。     

枚举有符号基因组的可行交互移位算法

交互移位排序问题(SRT)是寻找一个使一个基因组转变为另一个基因组的最短交互移位序列。现在已有多个多项式时间的SRT算法,但大多数问题实例都有许多个最短交互移位序列,因此寻找所有最短交互移位序列问题是SRT一个自然的推广。这个问题可以归约为寻找一个基因组相对于另一个基因组的全部可行交互移位,即所有移位ρ满足:在一个基因组上执行ρ之后,所得基因组相对于另一个基因组的移位距离会减少。本文提出一个用来寻找全部可行交互移位的有效算法,尽管新算法的时间复杂度比穷举法改进不大,但实验结果表明,其在实际运行中表现更好。     

单向链表快速排序算法

单向链表广泛应用于动态存储结构,当前单向链表的排序算法普遍效率偏低,而平均效率最高的快速排序算法并不适用于单向链表。基于分治策略,使用递归方法,通过重新链接单向链表节点,提出了用于单向链表的快速排序算法,其平均时间复杂度为O(nlog2n),辅助空间复杂度为O(0),平均递归栈空间复杂度为O(log2n);同时,进行了算法分析和实验测试,其效率较其它单向链表排序算法有较大提高,且较传统基于线性表的快速排序算法也有一定提高。研究结果解决了当前单向链表排序效率较低的问题。     

模糊聚类计算的最佳算法

给出模糊关系传递闭包在对应模糊图上的几何意义,并提出一个基于图连通分支计算的模糊聚类最佳算法.对任给的n个样本,新算法最坏情况下的时间复杂性函数T(n)满足O(n)≤T(n)≤O(n²).与经典的基于模糊传递闭包计算的模糊聚类算法的O(n³logn)计算时间相比,新算法至少降低了O(n相似文献   

Two algorithms for a reachability problem in one-dimensional space

Two algorithms are proposed to solve a reachability problem among time-dependent obstacles in 1D space. In the first approach, the motion planning problem is reduced to a path existence problem in a directed graph. The algorithm is very simple, with running time O(n²), where n is the complexity of obstacles in space-time. The second algorithm uses a sweep-line technique and has running time O(n log₂ n). Besides, the latter algorithm can be easily modified to compute a collision-free trajectory, if such trajectories exist     

A Space and Time Efficient Algorithm for Constructing Compressed Suffix Arrays

With the first human DNA being decoded into a sequence of about 2.8 billion characters, much biological research has been centered on analyzing this sequence. Theoretically speaking, it is now feasible to accommodate an index for human DNA in the main memory so that any pattern can be located efficiently. This is due to the recent breakthrough on compressed suffix arrays, which reduces the space requirement from O(n log n) bits to O(n) bits. However, constructing compressed suffix arrays is still not an easy task because we still have to compute suffix arrays first and need a working memory of O(n log n) bits (i.e., more than 13 gigabytes for human DNA). This paper initiates the study of constructing compressed suffix arrays directly from the text. The main contribution is a construction algorithm that uses only O(n) bits of working memory, and the time complexity is O(n log n). Our construction algorithm is also time and space efficient for texts with large alphabets such as Chinese or Japanese. Precisely, when the alphabet size is |Σ|, the working space is O(n log |Σ|) bits, and the time complexity remains O(n log n), which is independent of |Σ|.