LCS approximation via embedding into locally non-repetitive strings

A classical measure of similarity between strings is the length of the longest common subsequence (LCS) between the two given strings. The search for efficient algorithms for finding the LCS has been going on for more than three decades. To date, all known algorithms may take quadratic time (shaved by logarithmic factors) to find large LCS. In this paper, the problem of approximating LCS is studied, while focusing on the hard inputs for this problem, namely, approximating LCS of near-linear size in strings over a relatively large alphabet (of size at least n^? for some constant ?>0, where n is the length of the string). We show that, any given string over a relatively large alphabet can be embedded into a locally non-repetitive string. This embedding has a negligible additive distortion for strings that are not too dissimilar in terms of the edit distance. We also show that LCS can be efficiently approximated in locally-non-repetitive strings. Our new method (the embedding together with the approximation algorithm) gives a strictly sub-quadratic time algorithm (i.e., of complexity O(n^2-?) for some constant ?) which can find common subsequences of linear (and near linear) size that cannot be detected efficiently by the existing tools.     

Sparse LCS Common Substring Alignment

The “Common Substring Alignment” problem is defined as follows. The input consists of a set of strings S₁,S₂…,S_c, with a common substring appearing at least once in each of them, and a target string T. The goal is to compute similarity of all strings S_i with T, without computing the part of the common substring over and over again.In this paper we consider the Common Substring Alignment problem for the LCS (Longest Common Subsequence) similarity metric. Our algorithm gains its efficiency by exploiting the sparsity inherent to the LCS problem. Let Y be the common substring, n be the size of the compared sequences, L_y be the length of the LCS of T and Y, denoted |LCS[T,Y]|, and L be max{|LCS[T,S_i]|}. Our algorithm consists of an O(nL_y) time encoding stage that is executed once per common substring, and an O(L) time alignment stage that is executed once for each appearance of the common substring in each source string. The additional running time depends only on the length of the parts of the strings that are not in any common substring.     

Longest common subsequence between run-length-encoded strings: a new algorithm with improved parallelism

Data compression can be used to simultaneously reduce memory, communication and computation requirements of string comparison. In this paper we address the problem of computing the length of the longest common subsequence (LCS) between run-length-encoded (RLE) strings. We exploit RLE both to reduce the complexity of LCS computation from O(M×N) to O(mN+Mn−mn), where M and N are the lengths of the original strings and m and n the number of runs in their RLE representation, and to improve the inherent parallelism of the proposed algorithm, so that it may execute in O(m+n) steps on a systolic array of M+N units.We also discuss the application of the proposed algorithm to the related problem of edit distance (ED) computation.     

A dynamic programming solution to a generalized LCS problem

In this paper, we consider a generalized longest common subsequence problem, the string-excluding constrained LCS problem. For the two input sequences X and Y of lengths n and m, and a constraint string P of length r, the problem is to find a common subsequence Z of X and Y excluding P as a substring and the length of Z is maximized. The problem and its solution were first proposed by Chen and Chao (2011) [1], but we found that their algorithm cannot solve the problem correctly. A new dynamic programming solution for the STR-EC-LCS problem is then presented in this paper. The correctness of the new algorithm is proved. The time complexity of the new algorithm is $O(nmr)$.     

A linear space algorithm for the LCS problem

Summary The LCS problem is to determine the longest common subsequence (LCS) of two strings. A new linear-space algorithm to solve the LCS problem is presented. The only other algorithm with linear-space complexity is by Hirschberg and has runtime complexity O(mn). Our algorithm, based on the divide and conquer technique, has runtime complexity O(n(m-p)), where p is the length of the LCS.     

Fast Arc-Annotated Subsequence Matching in Linear Space

An arc-annotated string is a string of characters, called bases, augmented with a set of pairs, called arcs, each connecting two bases. Given arc-annotated strings P and Q the arc-preserving subsequence problem is to determine if P can be obtained from Q by deleting bases from Q. Whenever a base is deleted any arc with an endpoint in that base is also deleted. Arc-annotated strings where the arcs are “nested” are a natural model of RNA molecules that captures both the primary and secondary structure of these. The arc-preserving subsequence problem for nested arc-annotated strings is basic primitive for investigating the function of RNA molecules. Gramm et al. (ACM Trans. Algorithms 2(1): 44–65, 2006) gave an algorithm for this problem using O(nm) time and space, where m and n are the lengths of P and Q, respectively. In this paper we present a new algorithm using O(nm) time and O(n+m) space, thereby matching the previous time bound while significantly reducing the space from a quadratic term to linear. This is essential to process large RNA molecules where the space is likely to be a bottleneck. To obtain our result we introduce several novel ideas which may be of independent interest for related problems on arc-annotated strings.     

Efficient algorithms for finding interleaving relationship between sequences

The longest common subsequence and sequence alignment problems have been studied extensively and they can be regarded as the relationship measurement between sequences. However, most of them treat sequences evenly or consider only two sequences. Recently, with the rise of whole-genome duplication research, the doubly conserved synteny relationship among three sequences should be considered. It is a brand new model to find a merging way for understanding the interleaving relationship of sequences. Here, we define the merged LCS problem for measuring the interleaving relationship among three sequences. An O(n³) algorithm is first proposed for solving the problem, where n is the sequence length. We further discuss the variant version of this problem with the block information. For the blocked merged LCS problem, we propose an algorithm with time complexity O(n²m), where m is the number of blocks. An improved O(n²+nm²) algorithm is further proposed for the same blocked problem.     

Towards optimal range medians

We consider the following problem: Given an unsorted array of n elements, and a sequence of intervals in the array, compute the median in each of the subarrays defined by the intervals. We describe a simple algorithm which needs O(nlogk+klogn) time to answer k such median queries. This improves previous algorithms by a logarithmic factor and matches a comparison lower bound for k=O(n). The space complexity of our simple algorithm is O(nlogn) in the pointer machine model, and O(n) in the RAM model. In the latter model, a more involved O(n) space data structure can be constructed in O(nlogn) time where the time per query is reduced to O(logn/loglogn). We also give efficient dynamic variants of both data structures, achieving O(log²n) query time using O(nlogn) space in the comparison model and O((logn/loglogn)²) query time using O(nlogn/loglogn) space in the RAM model, and show that in the cell-probe model, any data structure which supports updates in O(log^O(1)n) time must have Ω(logn/loglogn) query time.Our approach naturally generalizes to higher-dimensional range median problems, where element positions and query ranges are multidimensional—it reduces a range median query to a logarithmic number of range counting queries.     

A New Efficient Algorithm for Computing the Longest Common Subsequence

The Longest Common Subsequence (LCS) problem is a classic and well-studied problem in computer science. The LCS problem is a common task in DNA sequence analysis with many applications to genetics and molecular biology. In this paper, we present a new and efficient algorithm for solving the LCS problem for two strings. Our algorithm runs in O(ℛlog log n+n) time, where ℛ is the total number of ordered pairs of positions at which the two strings match. Preliminary version appeared in [24]. C.S. Iliopoulos is supported by EPSRC and Royal Society grants. M.S. Rahman is supported by the Commonwealth Scholarship Commission in the UK under the Commonwealth Scholarship and Fellowship Plan (CSFP) and is on Leave from Department of CSE, BUET, Dhaka-1000, Bangladesh.     

Parallel String Matching with Variable Length Don′t Cares

String matching is the problem of finding all the occurrences of a pattern P in a text T, where P and T are strings over a finite alphabet Σ. A variable length don′t care is a special character, not belonging to Σ, which can match any string in Σ*. The string-matching problem with variable length don′t cares is an extension of the classical string-matching problem in which the pattern P may contain an arbitrary number of don′t cares. An efficient parallel algorithm is given for solving the string-matching problem with variable length don′t cares. The EREW PRAM model of parallel computer with scan operations is used to obtain an O(log n) running time using O(mn/log n) processors, where m and n are, respectively, the lengths of P and T. The proposed parallel algorithm has an Ω(1/log n) processor utilization, since the fastest serial algorithm known so far has an O(mn/log n) running time.     

A longest common subsequence algorithm suitable for similar text strings

Summary Efficient algorithms for computing the longest common subsequence (LCS for short) are discussed. O(pn) algorithm and O(p(m-p) log n) algorithm [Hirschberg 1977] seem to be best among previously known algorithms, where p is the length of an LCS and m and n are the lengths of given two strings (mn). There are many applications where the expected length of an LCS is close to m.In this paper, O(n(m-p)) algorithm is presented. When p is close to m (in other words, two given strings are similar), the algorithm presented here runs much faster than previously known algorithms.     

A fast algorithm for finding the positions of all squares in a run-length encoded string

Squares are strings of the form ww where w is any nonempty string. Main and Lorentz proposed an O(nlogn)-time algorithm for finding the positions of all squares in a string of length n. Based on their result, we show how to find the positions of all squares in a run-length encoded string in time O(NlogN) where N is the number of runs in this string, provided that we do not explicitly compute at all “trivial squares” occurring within runs. The algorithm is optimal and its time complexity is independent of the length of the original uncompressed string.     

The longest common subsequence problem revisited

This paper re-examines, in a unified framework, two classic approaches to the problem of finding a longest common subsequence (LCS) of two strings, and proposes faster implementations for both. Letl be the length of an LCS between two strings of lengthm andn ≥m, respectively, and let s be the alphabet size. The first revised strategy follows the paradigm of a previousO(ln) time algorithm by Hirschberg. The new version can be implemented in timeO(lm · min logs, logm, log(2n/m)), which is profitable when the input strings differ considerably in size (a looser bound for both versions isO(mn)). The second strategy improves on the Hunt-Szymanski algorithm. This latter takes timeO((r +n) logn), wherer≤mn is the total number of matches between the two input strings. Such a performance is quite good (O(n logn)) whenr～n, but it degrades to Θ(mn logn) in the worst case. On the other hand the variation presented here is never worse than linear-time in the productmn. The exact time bound derived for this second algorithm isO(m logn +d log(2mn/d)), whered ≤r is the number ofdominant matches (elsewhere referred to asminimal candidates) between the two strings. Both algorithms require anO(n logs) preprocessing that is nearly standard for the LCS problem, and they make use of simple and handy auxiliary data structures.     

Computing the Map of Geometric Minimal Cuts

In this paper we consider the following problem of computing a map of geometric minimal cuts (called MGMC problem): Given a graph G=(V,E) and a planar rectilinear embedding of a subgraph H=(V _H ,E _H ) of G, compute the map of geometric minimal cuts induced by axis-aligned rectangles in the embedding plane. The MGMC problem is motivated by the critical area extraction problem in VLSI designs and finds applications in several other fields. In this paper, we propose a novel approach based on a mix of geometric and graph algorithm techniques for the MGMC problem. Our approach first shows that unlike the classic min-cut problem on graphs, the number of all rectilinear geometric minimal cuts is bounded by a low polynomial, O(n ³). Our algorithm for identifying geometric minimal cuts runs in O(n ³logn(loglogn)³) expected time which can be reduced to O(nlogn(loglogn)³) when the maximum size of the cut is bounded by a constant, where n=|V _H |. Once geometric minimal cuts are identified we show that the problem can be reduced to computing the L _∞ Hausdorff Voronoi diagram of axis aligned rectangles. We present the first output-sensitive algorithm to compute this diagram which runs in O((N+K)log² NloglogN) time and O(Nlog² N) space, where N is the number of rectangles and K is the complexity of the Hausdorff Voronoi diagram. Our approach settles several open problems regarding the MGMC problem.     

On-line construction of parameterized suffix trees for large alphabets

We consider on-line construction of the suffix tree for a parameterized string, where we always have the suffix tree of the input string read so far. This situation often arises from source code management systems where, for example, a source code repository is gradually increasing in its size as users commit new codes into the repository day by day. We present an on-line algorithm which constructs a parameterized suffix tree in randomized O(n) time, where n is the length of the input string. Our algorithm is the first randomized linear time algorithm for the on-line construction problem.     

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.     

A randomized sublinear time parallel GCD algorithm for the EREW PRAM

We present a randomized parallel algorithm that computes the greatest common divisor of two integers of n bits in length with probability 1−o(1) that takes O(nloglogn/logn) time using O(n6+?) processors for any ?>0 on the EREW PRAM parallel model of computation. The algorithm either gives a correct answer or reports failure.We believe this to be the first randomized sublinear time algorithm on the EREW PRAM for this problem.     

Unified Compression-Based Acceleration of Edit-Distance Computation

The edit distance problem is a classical fundamental problem in computer science in general, and in combinatorial pattern matching in particular. The standard dynamic programming solution for this problem computes the edit-distance between a pair of strings of total length O(N) in O(N ²) time. To this date, this quadratic upper-bound has never been substantially improved for general strings. However, there are known techniques for breaking this bound in case the strings are known to compress well under a particular compression scheme. The basic idea is to first compress the strings, and then to compute the edit distance between the compressed strings. As it turns out, practically all known o(N ²) edit-distance algorithms work, in some sense, under the same paradigm described above. It is therefore natural to ask whether there is a single edit-distance algorithm that works for strings which are compressed under any compression scheme. A rephrasing of this question is to ask whether a single algorithm can exploit the compressibility properties of strings under any compression method, even if each string is compressed using a different compression. In this paper we set out to answer this question by using straight line programs. These provide a generic platform for representing many popular compression schemes including the LZ-family, Run-Length Encoding, Byte-Pair Encoding, and dictionary methods. For two strings of total length N having straight-line program representations of total size n, we present an algorithm running in O(nNlg(N/n)) time for computing the edit-distance of these two strings under any rational scoring function, and an O(n ^2/3 N ^4/3) time algorithm for arbitrary scoring functions. Our new result, while providing a speed up for compressible strings, does not surpass the quadratic time bound even in the worst case scenario.     

Improved Bounds for Finger Search on a RAM

We present a new finger search tree with O(loglogd) expected search time in the Random Access Machine (RAM) model of computation for a large class of input distributions. The parameter d represents the number of elements (distance) between the search element and an element pointed to by a finger, in a finger search tree that stores n elements. Our data structure improves upon a previous result by Andersson and Mattsson that exhibits expected O(loglogn) search time by incorporating the distance d into the search time complexity, and thus removing the dependence on n. We are also able to show that the search time is O(loglogd+?(n)) with high probability, where ?(n) is any slowly growing function of n. For the need of the analysis we model the updates by a “balls and bins” combinatorial game that is interesting in its own right as it involves insertions and deletions of balls according to an unknown distribution.     

Cache-oblivious index for approximate string matching

This paper revisits the problem of indexing a text for approximate string matching. Specifically, given a text T of length n and a positive integer k, we want to construct an index of T such that for any input pattern P, we can find all its k-error matches in T efficiently. This problem is well-studied in the internal-memory setting. Here, we extend some of these recent results to external-memory solutions, which are also cache-oblivious. Our first index occupies O((nlog^kn)/B) disk pages and finds all k-error matches with O((|P|+occ)/B+log^knloglog_Bn) I/Os, where B denotes the number of words in a disk page. To the best of our knowledge, this index is the first external-memory data structure that does not require Ω(|P|+occ+poly(logn)) I/Os. The second index reduces the space to O((nlogn)/B) disk pages, and the I/O complexity is O((|P|+occ)/B+log^k(k+1)nloglogn).