求解大规模谱聚类的近似加权核k-means算法

谱聚类将聚类问题转化成图划分问题,是一种基于代数图论的聚类方法.在求解图划分目标函数时,一般利用Rayleigh熵的性质,通过计算Laplacian矩阵的特征向量将原始数据点映射到一个低维的特征空间中,再进行聚类.然而在谱聚类过程中,存储相似矩阵的空间复杂度是O(n²),对Laplacian矩阵特征分解的时间复杂度一般为O(n³),这样的复杂度在处理大规模数据时是无法接受的.理论证明,Normalized Cut图聚类与加权核k-means都等价于矩阵迹的最大化问题.因此,可以用加权核k-means算法来优化Normalized Cut的目标函数,这就避免了对Laplacian矩阵特征分解.不过,加权核k-means算法需要计算核矩阵,其空间复杂度依然是O(n²).为了应对这一挑战,提出近似加权核k-means算法,仅使用核矩阵的一部分来求解大数据的谱聚类问题.理论分析和实验对比表明,近似加权核k-means的聚类表现与加权核k-means算法是相似的,但是极大地减小了时间和空间复杂性.     

加权3-Set Packing 的改进算法

Packing 问题构成了一类重要的NP 难问题.对于加权3-Set Packing 问题,把问题转化成加权3-Set Packing Augmentation 问题进行求解,即主要讨论如何从一个已知的最大加权k-packing 求得一个权值最大的(k+1)-packing. 通过对问题结构的分析,结合Color-Coding 技术,首先给出了一种时间复杂度为O^*(10.6^3k)的参数算法,极大地改进了目前文献中的最好结果O^*(12.8^3k).通过对(k+1)-packing 结构的进一步分析,利用集合划分技术将上述结果降到O^*(7.56^3k).     

k-Median近似计算复杂度与局部搜索近似算法分析

k-Median问题的近似算法研究一直是计算机科学工作者关注的焦点,现有研究结果大多是关于欧式空间和Metric空间的,一般距离空间k-Median的结果多年来一直未见.考虑一般距离空间k-Median问题,设d_max/d_min表示k-Median实例中与客户点邻接的最长边长比最短边长的最大者.首先证明d_max/d_min≤ω+ε的k-Median问题不存在近似度小于1+ω-1/e的多项式时间近似算法,除非,由此推出Metric k-Median问题不可近似到1+2/e,除非NP(∈)DTME(N^{O(log logn)}).然后给出k-Median问题的一个局部搜索算法,分析表明,若有d_max/d_min≤ω,则算法的近似度为1+ω-1/2.该结果亦适用于Metric k-Median,ω≤5时,局部搜索算法求解Metric k-Median的近似度为3,好于现有结果3+2/P.通过计算机实验,进一步研究了k-Median局部搜索求解算法的实际计算效果和该算法的改进方法.     

光RP(k)网络上Hypercube通信模式的波长指派算法

波长指派是光网络设计的基本问题,设计波长指派算法是洞察光网络通信能力的基本方法.基于光RP(k)网络,讨论了其波长指派问题. 含有N=2ⁿ个节点的Hypercube通信模式,构造了节点间的一种排列次序Xn,并设计了RP(k)网络上的波长指派算法.在构造该算法的过程中,得到了在环网络上实现n维Hypercube通信模式的波长指派算法.这两个算法具有较高的嵌入效率.在RP(k)网络上,实现Hypercube通信模式需要max{2,「5(2^n-5/3」}个波长.而在环网络上,实现该通信模式需要复用(N/3+N/12(个波长,比已有算法需要复用「N/3+N/4」个波长有较大的改进.这两个算法对于光网络的设计具有较大的指导价值.     

网格多处理机的一种改进的子网分配算法

子网分配问题是指识别并分配一个空闲的、满足指定大小要求的节点机.首先,提出了网格结构中一种新的具有O(N²_a·1og₂N_a)时间复杂度的空闲子网搜索算法,它优于现有的O(N³_a)时间复杂度的搜索算法.然后,用该算法对基于保留因子的最佳匹配类子网分配算法——RF(reservation factor)算法进行了改进,得到了     

一类可分离SAT问题的O(1.890n)精确算法

布尔可满足性问题（SAT）是指对于给定的布尔公式,是否存在一个可满足的真值指派.这是第1个被证明的NP完全问题,一般认为不存在多项式时间算法,除非P=NP.学者们大都研究了子句长度不超过k的SAT问题（k-SAT）,从全局搜索到局部搜索,给出了大量的相对有效算法,包括随机算法和确定算法.目前,最好算法的时间复杂度不超过O（（2-2/k）ⁿ）,当k=3时,最好算法时间复杂度为O（1.308ⁿ）.而对于更一般的与子句长度k无关的SAT问题,很少有文献涉及.引入了一类可分离SAT问题,即3-正则可分离可满足性问题（3-RSSAT）,证明了3-RSSAT是NP完全问题,给出了一般SAT问题3-正则可分离性的O（1.890ⁿ）判定算法.然后,利用矩阵相乘算法的研究成果,给出了3-RSSAT问题的O（1.890ⁿ）精确算法,该算法与子句长度无关.     

有Mate-Pairs的个体单体型MSR问题的参数化算法

个体单体型MSR(minimum SNP removal)问题是指如何利用个体的基因测序片断数据去掉最少的SNP(single-nucleotide polymorphisms)位点,以确定该个体单体型的计算问题.对此问题,Bafna等人提出了时间复杂度为O(2^kn²m)的算法,其中,m为DNA片断总数,n为SNP位点总数,k为片断中洞(片断中的空值位点)的个数.由于一个Mate-Pair片段中洞的个数可以达到100,因此,在片段数据中有Mate-Pair的情况下,Bafna的算法通常是不可行的.根据片段数据的特点提出了一个时间复杂度为O((n-1)(k₁-1)k₂2^2h+(k₁+1)^2h+nk₂+mk₁)的新算法,其中,k₁为一个片断覆盖的最大SNP位点数(不大于n),k₂为覆盖同一SNP位点的片段的最大数(通常不大于19),h为覆盖同一SNP位点且在该位点取空值的片断的最大数(不大于k₂).该算法的时间复杂度与片断中洞的个数的最大值k没有直接的关系,在有Mate-Pair片断数据的情况下仍然能够有效地进行计算,具有良好的可扩展性和较高的实用价值.     

改进k值自动获取VDBSCAN聚类算法

针对DBSCAN聚类算法不能对变密度分布数据集进行有效聚类,VDBSCAN算法借助k-dist图来自动获取各个密度层次的数据对象的邻域半径,解决了具有不同密度层次分布数据集的聚类问题. k-VDBSCAN算法通过对k值的自动获取,减小了VDBSCAN中参数k对最终聚类结果的影响. 针对k值的自动获取,在原有的k-VDBSCAN聚类算法基础上,依据数据集本身,利用数据对象间距离的特征,提出了一种k值改进自动获取聚类算法. 理论分析与实验结果表明,新的改进算法能够有效的自动获得参数k的值,并且在聚类结果、时间效率方面都有明显的提高.     

RNA二级结构预测中动态规划的优化和有效并行

基于最小自由能模型的方法是计算生物学中RNA二级结构预测的主要方法,而计算最小自由能的动态规划算法需要O(n⁴)的时间,其中n是RNA序列的长度.目前有两种降低时间复杂度的策略:限制二级结构中内部环的大小不超过k,得到O(n²×k²)算法;Lyngso方法根据环的能量规则,不限制环的大小,在O(n3)的时间内获得近似最优解.通过使用额外的O(n)的空间,计算内部环中的冗余计算大为减少,从而在同样不限制环大小的情况下,在O(n³)的时间内能够获得最优解.然而,优化后的算法仍然非常耗时,通过有效的负载平衡方法,在机群系统上实现并行程序.实验结果表明,并行程序获得了很好的加速比.     

半动态矩形交查询算法

本文讨论了动态矩形交查询算法.文中介绍了两个半动态矩形查询的新算法，它们分别基于一维数据结构和二维数据结构.一维查询算法的查询时间复杂度是O（logM＋k′），更新时间复杂度是O（logMlogn），空间复杂度是O（nlogM/）.二维查询算法的查询时间复杂度是O（log²M＋k），更新时间复杂度是O（log²Mlogn），空间复杂度是O（nlog²M）.本文分别实现了这两个算法，通过对它们的性能进行比较，发现一维查询算法是一种高效、实用的算法.     

Improved Parameterized Set Splitting Algorithms: A Probabilistic Approach

In this paper, we study parameterized algorithms for the set splitting problem, for both weighted and unweighted versions. First, we develop a new and effective technique based on a probabilistic method that allows us to develop a simpler and more efficient deterministic kernelization algorithm for the unweighted set splitting problem. We then propose a randomized algorithm for the weighted set splitting problem that is based on a new subset partition technique and has its running time bounded by O ^*(2 ^k ), which is significantly better than that of the previous best deterministic algorithm (which only works for the simpler unweighted set splitting problem) of running time O ^*(2.65 ^k ). We also show that our algorithm can be de-randomized, which leads to a deterministic parameterized algorithm of running time O ^*(4 ^k ) for the weighted set splitting problem and gives the first proof that the problem is fixed-parameter tractable. A preliminary version of this paper was presented at The 13th Annual International Computing and Combinatorics Conference (COCOON 2007), Banff, Canada, July 2007, LNCS vol. 4598, pp. 537–547. This work was supported in part by the National Science Foundation under the Grant CCF-0430683.     

Improved deterministic algorithms for weighted matching and packing problems

Based on the method of (n,k)-universal sets, we present a deterministic parameterized algorithm for the weighted rd-matching problem with time complexity O^∗(4^{(r−1)k+o(k)}), improving the previous best upper bound O^∗(4^rk+o(k)). In particular, the algorithm applied to the unweighted 3d-matching problem results in a deterministic algorithm with time O^∗(16^k+o(k)), improving the previous best result O^∗(21.26^k). For the weighted r-set packing problem, we present a deterministic parameterized algorithm with time complexity O^∗(2^{(2r−1)k+o(k)}), improving the previous best result O^∗(2^2rk+o(k)). The algorithm, when applied to the unweighted 3-set packing problem, has running time O^∗(32^k+o(k)), improving the previous best result O^∗(43.62^k+o(k)). Moreover, for the weighted r-set packing and weighted rd-matching problems, we give a kernel of size O(k^r), which is the first kernelization algorithm for the problems on weighted versions.     

Kernels for feedback arc set in tournaments

A tournament T=(V,A) is a directed graph in which there is exactly one arc between every pair of distinct vertices. Given a digraph on n vertices and an integer parameter k, the Feedback Arc Set problem asks whether the given digraph has a set of k arcs whose removal results in an acyclic digraph. The Feedback Arc Set problem restricted to tournaments is known as the k-Feedback Arc Set in Tournaments (k-FAST) problem. In this paper we obtain a linear vertex kernel for k-FAST. That is, we give a polynomial time algorithm which given an input instance T to k-FAST obtains an equivalent instance T^′ on O(k) vertices. In fact, given any fixed ?>0, the kernelized instance has at most (2+?)k vertices. Our result improves the previous known bound of O(k²) on the kernel size for k-FAST. Our kernelization algorithm solves the problem on a subclass of tournaments in polynomial time and uses a known polynomial time approximation scheme for k-FAST.     

A linear-time algorithm for finding a sparsek-connected spanning subgraph of ak-connected graph

We show that anyk-connected graphG = (V, E) has a sparsek-connected spanning subgraphG = (V, E) with ¦E¦ =O(k¦V¦) by presenting anO(¦E¦)-time algorithm to find one such subgraph, where connectivity stands for either edge-connectivity or node-connectivity. By using this algorithm as preprocessing, the time complexities of some graph problems related to connectivity can be improved. For example, the current best time boundO(max{k ²¦V¦^1/2,k¦V¦}¦E¦) to determine whether node-connectivityK(G) of a graphG = (V, E) is larger than a given integerk or not can be reduced toO(max{k ³¦V¦^3/2,k ²¦V¦²}).The first author was partially supported by the Grant-in-Aid for Encouragement of Young Scientists of the Ministry of Education, Science and Culture of Japan and by the subvention to young scientists by the Research Foundation of Electrotechnology of Chubu.     

Quadratic Kernelization for Convex Recoloring of Trees

The Convex Recoloring (CR) problem measures how far a tree of characters differs from exhibiting a so-called “perfect phylogeny”. For an input consisting of a vertex-colored tree T, the problem is to determine whether recoloring at most k vertices can achieve a convex coloring, meaning by this a coloring where each color class induces a subtree. The problem was introduced by Moran and Snir (J. Comput. Syst. Sci. 73:1078–1089, 2007; J. Comput. Syst. Sci. 74:850–869, 2008) who showed that CR is NP-hard, and described a search-tree based FPT algorithm with a running time of O(k(k/log k) ^k n ⁴). The Moran and Snir result did not provide any nontrivial kernelization. In this paper, we show that CR has a kernel of size O(k ²).     

P2-Packing问题参数算法的改进

P_2-Packing问题是一个典型的NP难问题.目前这个问题的最好结果是时间复杂度为O(2~(5.301k))的参数算法,其核的大小为15k.通过对P_2-packing问题的结构作进一步分析,提出了改进的核心化算法,得到大小为7k的核,并在此基础上提出了一种时间复杂度为O(2~(4.142k))的参数算法,大幅度改进了目前文献中的最好结果.     

A linear-time algorithm for linearL1 approximation of points

In this paper we present a linear-time algorithm for approximating a set ofn points by a linear function, or a line, that minimizes theL ₁ norm. The algorithmic complexity of this problem appears not to have been investigated, although anO(n ³) naive algorithm can be easily obtained based on some simple characteristics of an optimumL ₁ solution. Our linear-time algorithm is optimal within a constant factor and enables us to use linearL ₁ approximation of many points in practice. The complexity ofL ₁ linear approximation of a piecewise linear function is also touched upon.