Speeding up transposition-invariant string matching

Finding the longest common subsequence (LCS) of two given sequences A=a₀a₁…am−1 and B=b₀b₁…bn−1 is an important and well studied problem. We consider its generalization, transposition-invariant LCS (LCTS), which has recently arisen in the field of music information retrieval. In LCTS, we look for the LCS between the sequences A+t=(a₀+t)(a₁+t)…(am−1+t) and B where t is any integer. We introduce a family of algorithms (motivated by the Hunt-Szymanski scheme for LCS), improving the currently best known complexity from O(mnloglogσ) to O(Dloglogσ+mn), where σ is the alphabet size and D?mn is the total number of dominant matches for all transpositions. Then, we demonstrate experimentally that some of our algorithms outperform the best ones from literature.     

Optimal algorithms for the average-constrained maximum-sum segment problem

Given a real number sequence A=(a₁,a₂,…,an), an average lower bound L, and an average upper bound U, the Average-Constrained Maximum-Sum Segment problem is to locate a segment A(i,j)=(ai,ai+1,…,aj) that maximizes _∑i?k?jak subject to . In this paper, we give an O(n)-time algorithm for the case where the average upper bound is ineffective, i.e., U=∞. On the other hand, we prove that the time complexity of the problem with an effective average upper bound is Ω(nlogn) even if the average lower bound is ineffective, i.e., L=−∞.     

A fast and simple algorithm for computing the longest common subsequence of run-length encoded strings

Let X and Y be two strings of lengths n and m, respectively, and k and l, respectively, be the numbers of runs in their corresponding run-length encoded forms. We propose a simple algorithm for computing the longest common subsequence of two given strings X and Y in O(kl+min{p₁,p₂}) time, where p₁ and p₂ denote the numbers of elements in the bottom and right boundaries of the matched blocks, respectively. It improves the previously known time bound O(min{nl,km}) and outperforms the time bounds O(kllogkl) or O((k+l+q)log(k+l+q)) for some cases, where q denotes the number of matched blocks.     

Approximate regular expression pattern matching with concave gap penalties

Given a sequenceA of lengthM and a regular expressionR of lengthP, an approximate regular expression pattern-matching algorithm computes the score of the optimal alignment betweenA and one of the sequencesB exactly matched byR. An alignment between sequencesA=a₁a₂ ... a_M andB=b₁b₂... b_N is a list of ordered pairs, (i₁,j₁), (i₂j₂), ..., (i_t,j_tt) such that i_k < i_k+1 and j_k < j_k+1. In this case the alignmentaligns symbols a_ik and b_jk, and leaves blocks of unaligned symbols, orgaps, between them. A scoring schemeS associates costs for each aligned symbol pair and each gap. The alignment's score is the sum of the associated costs, and an optimal alignment is one of minimal score. There are a variety of schemes for scoring alignments. In a concave gap penalty scoring schemeS={, w}, a function (a, b) gives the score of each aligned pair of symbolsa andb, and aconcave function w(k) gives the score of a gap of lengthk. A function w is concave if and only if it has the property that, for allk > 1, w(k + 1) –w(k) w(k) –w(k –1). In this paper we present an O(MP(logM + log² P)) algorithm for approximate regular expression matching for an arbitrary and any concavew. This work was supported in part by the National Institute of Health under Grant RO1 LM04960.     

The periodic nature of the positive solutions of a nonlinear fuzzy max-difference equation

We investigate the periodic nature of the positive solutions of the fuzzy difference equation , where k, m are positive integers, A₀, A₁, are positive fuzzy numbers and the initial values x_i, i = −d, −d + 1, … , −1, d = max{k, m}, are positive fuzzy numbers. In addition, we give conditions so that the solutions of this equation are unbounded.     

Global roundings of sequences

For a given sequence a=(a₁,…,an) of numbers, a global rounding is an integer sequence b=(b₁,…,bn) such that the rounding error |_∑i∈I(ai−bi)| is less than one in all intervals I⊆{1,…,n}. We give a simple characterization of the set of global roundings of a. This allows to compute optimal roundings in time O(nlogn) and generate a global rounding uniformly at random in linear time under a non-degeneracy assumption and in time O(nlogn) in the general case.     

A Time-Optimal Multiple Search Algorithm on Enhanced Meshes,with Applications

Given a sorted sequence A = a₁, a₂, ..., a_n of items from a totally ordered universe, along with an arbitrary sequence Q = q₁, q₂, ..., q_m (1 ≤ m ≤ n) of queries, the multiple search problem involves computing for every q_j (1 ≤ j ≤ m) the unique a_i for which a_i−1 ≤ q_j < a_i. In this paper we propose a time-optimal algorithm to solve the multiple search problem on meshes with multiple broadcasting. More specifically, on a [formula] × [formula] mesh with multiple broadcasting, our algorithm runs in [formula] time which is shown to be time-optimal. We also present some surprising applications of the multiple search algorithm to computer graphics, image processing, robotics, and computational geometry.     

Multiple serial episodes matching

Given q+1 strings (a text t of length n and q patterns m₁,…,mq) and a natural number w, the multiple serial episode matching problem consists in finding the number of size w windows of text t which contain patterns m₁,…,mq as subsequences, i.e., for each mi, if mi=p₁,…,pk, the letters p₁,…,pk occur in the window, in the same order as in mi, but not necessarily consecutively (they may be interleaved with other letters). Our main contribution here is an algorithm solving this problem on-line in time O(nq) with an MP-RAM model (which is a RAM model equipped with extra operations).     

On the cubicity of bipartite graphs

A unit cube in k-dimension (or a k-cube) is defined as the Cartesian product R₁×R₂×?×Rk, where each Ri is a closed interval on the real line of the form [ai,ai+1]. The cubicity of G, denoted as cub(G), is the minimum k such that G is the intersection graph of a collection of k-cubes. Many NP-complete graph problems can be solved efficiently or have good approximation ratios in graphs of low cubicity. In most of these cases the first step is to get a low dimensional cube representation of the given graph.It is known that for a graph G, . Recently it has been shown that for a graph G, cub(G)?4(Δ+1)lnn, where n and Δ are the number of vertices and maximum degree of G, respectively. In this paper, we show that for a bipartite graph G=(A∪B,E) with |A|=n₁, |B|=n₂, n₁?n₂, and Δ^′=min{ΔA,ΔB}, where ΔA=maxa∈Ad(a) and ΔB=maxb∈Bd(b), d(a) and d(b) being the degree of a and b in G, respectively, cub(G)?2(Δ^′+2)⌈lnn₂⌉. We also give an efficient randomized algorithm to construct the cube representation of G in 3(Δ^′+2)⌈lnn₂⌉ dimensions. The reader may note that in general Δ^′ can be much smaller than Δ.     

Approximation algorithms for covering/packing integer programs

Given matrices A and B and vectors a, b, c and d, all with non-negative entries, we consider the problem of computing . We give a bicriteria-approximation algorithm that, given ε∈(0,1], finds a solution of cost O(ln(m)/ε²) times optimal, meeting the covering constraints (Ax?a) and multiplicity constraints (x?d), and satisfying Bx?(1+ε)b+β, where β is the vector of row sums β_i=∑_jB_ij. Here m denotes the number of rows of A.This gives an O(lnm)-approximation algorithm for CIP—minimum-cost covering integer programs with multiplicity constraints, i.e., the special case when there are no packing constraints Bx?b. The previous best approximation ratio has been O(ln(max_j∑_iA_ij)) since 1982. CIP contains the set cover problem as a special case, so O(lnm)-approximation is the best possible unless P=NP.     

A note on computing set overlap classes

Let V be a finite set of n elements and F={X₁,X₂,…,Xm} a family of m subsets of V. Two sets Xi and Xj of F overlap if Xi∩Xj≠∅, Xj?Xi≠∅, and Xi?Xj≠∅. Two sets X,Y∈F are in the same overlap class if there is a series X=X₁,X₂,…,Xk=Y of sets of F in which each XiXi+1 overlaps. In this note, we focus on efficiently identifying all overlap classes in time. We thus revisit the clever algorithm of Dahlhaus [E. Dahlhaus, Parallel algorithms for hierarchical clustering and applications to split decomposition and parity graph recognition, J. Algorithms 36 (2) (2000) 205-240] of which we give a clear presentation and that we simplify to make it practical and implementable in its real worst case complexity. An useful variant of Dahlhaus's approach is also explained.     

Improved Generation of Minimal Addition Chains

An addition chain for a natural number n is a sequence 1=a ₀<a ₁< . . . <a _r =n of numbers such that for each 0<i≤r, a _i =a _j +a _k for some 0≤k≤j<i. An improvement by a factor of 2 in the generation of all minimal (or one) addition chains is achieved by finding sufficient conditions for star steps, computing what we will call nonstar lower bound in a minimal addition and omitting the sorting step.     

Thek-track assignment problem

Thek-track assignment problem is a scheduling problem withn jobs andk machines. Each machinej has a certain operational period (track) which starts at timea _j and ends at timeb _j . Each jobi has a specific start times _i and a specific finish timet _i . A schedule is an assignment of certain jobs to machines such that the intervals [s _i ,t _i [assigned to the same machinej do not overlap and fit into track [a _j ,b _j [. We are interested in a schedule which maximizes the number of assigned jobs. AO(n ^k?1 k!k ^k+1 )-algorithm which solves this problem is presented. Furthermore it is shown that the more general problem, in which for each track only a given set of jobs can be scheduled on that track, can be solved inO(n ^k k!k ^k )-time.     

Generalized L systems

This paper proposes the concept of generalized L systems, GL systems for short, which can describe asynchronized concurrent phenomena. We have proved that the GL systems are proper extensions of the traditional L systems. We have also defined a classification of GL systems and proved a sufficient and necessary condition for the equivalence of two subclasses of GL systems: two GPD0L (a class of deterministic GL systems) systems L[m ₁, m ₂, …, m _j ] and L[n ₁, n ₂, …, n _k ] are equivalent, iff k = j and there exists a common divisor g of all m _l and a common divisor h of all n _l such that ? i: m _i/g = n _i/h.     

A fully parallel method for the singular eigenvalue problem

In this paper, a fully parallel method for finding some or all finite eigenvalues of a real symmetric matrix pencil (A, B) is presented, where A is a symmetric tridiagonal matrix and B is a diagonal matrix with b₁ > 0 and b_i ≥ 0, i = 2,3,…,n. The method is based on the homotopy continuation with rank 2 perturbation. It is shown that there are exactly m disjoint, smooth homotopy paths connecting the trivial eigenvalues to the desired eigenvalues, where m is the number of finite eigenvalues of (A, B). It is also shown that the homotopy curves are monotonic and easy to follow.     

Partial realization of random systems

Consider a vector-valued stationary random process {Y_k}^∞_−∞, from which the estimates, R₀, R₁, …, R_N, of the covariance matrices EY_kY′_k−i, i=0, 1, 1, …, N, can be made.     

Resource scheduling with variable requirements over time

The problem of scheduling resources for tasks with variable requirements over time can be stated as follows. We are given two sequences of vectors A=A ₁,…,A _n and R=R ₁,…,R _m . Sequence A represents resource availability during n time intervals, where each vector A _i has q elements. Sequence R represents resource requirements of a task during m intervals, where each vector R _i has q elements. We wish to find the earliest time interval i, termed latency, such that for 1≤k≤m, 1≤j≤q: A _i+k−1 ^j ≥R _k ^j , where A _i+k−1 ^j and R _k ^j are the jth elements of vectors A _i+k−1 and R _k , respectively. One application of this problem is I/O scheduling for multimedia presentations. The fastest known algorithm to compute the optimal solution of this problem has computation time (Amir and Farach, in Proceedings of the ACM-SIAM symposium on discrete algorithms (SODA), San Francisco, CA, pp. 212–223, 1991; Inf. Comput. 118(1):1–11, 1995). We propose a technique that approximates the optimal solution in linear time: . We evaluated the performance of our algorithm when used for multimedia I/O scheduling. Our results show that 95% of the time, our solution is within 5% of the optimal.