Approximate Pattern Matching with the <Emphasis Type="Italic">L</Emphasis><Subscript>1</Subscript>, <Emphasis Type="Italic">L</Emphasis><Subscript>2</Subscript> and <Emphasis Type="Italic">L</Emphasis><Subscript>∞</Subscript> Metrics

Given an alphabet Σ={1,2,…,|Σ|} text string T∈Σ ⁿ and a pattern string P∈Σ ^m , for each i=1,2,…,n−m+1 define L _p (i) as the p-norm distance when the pattern is aligned below the text and starts at position i of the text. The problem of pattern matching with L _p distance is to compute L _p (i) for every i=1,2,…,n−m+1. We discuss the problem for d=1,2,∞. First, in the case of L ₁ matching (pattern matching with an L ₁ distance) we show a reduction of the string matching with mismatches problem to the L ₁ matching problem and we present an algorithm that approximates the L ₁ matching up to a factor of 1+ε, which has an O(\frac1e²nlogmlog|S|)O(\frac{1}{\varepsilon^{2}}n\log m\log|\Sigma|) run time. Then, the L ₂ matching problem (pattern matching with an L ₂ distance) is solved with a simple O(nlog m) time algorithm. Finally, we provide an algorithm that approximates the L _∞ matching up to a factor of 1+ε with a run time of O(\frac1enlogmlog|S|)O(\frac{1}{\varepsilon}n\log m\log|\Sigma|) . We also generalize the problem of String Matching with mismatches to have weighted mismatches and present an O(nlog ⁴ m) algorithm that approximates the results of this problem up to a factor of O(log m) in the case that the weight function is a metric.     

Lower Bounds for the Determinantal Complexity of Explicit Low Degree Polynomials

The determinantal complexity of a polynomial f(x ₁,x ₂,…,x _n ) is the minimum m such that f=det  _m (L(x ₁,x ₂,…,x _n )), where L(x ₁,x ₂,…,x _n ) is a matrix whose entries are affine forms in the x _i s over some field $\mbox {$\mbox {.     

On the solution of the fuzzy Sylvester matrix equation

In this paper, we consider the fuzzy Sylvester matrix equation AX+XB=C,AX+XB=C, where A ? \mathbbR^{n ×n}A\in {\mathbb{R}}^{n \times n} and B ? \mathbbR^{m ×m}B\in {\mathbb{R}}^{m \times m} are crisp M-matrices, C is an n×mn\times m fuzzy matrix and X is unknown. We first transform this system to an (mn)×(mn)(mn)\times (mn) fuzzy system of linear equations. Then, we investigate the existence and uniqueness of a fuzzy solution to this system. We use the accelerated over-relaxation method to compute an approximate solution to this system. Some numerical experiments are given to illustrate the theoretical results.     

Fast Algorithms for the Density Finding Problem

We study the problem of finding a specific density subsequence of a sequence arising from the analysis of biomolecular sequences. Given a sequence A=(a ₁,w ₁),(a ₂,w ₂),…,(a _n ,w _n ) of n ordered pairs (a _i ,w _i ) of numbers a _i and width w _i >0 for each 1≤i≤n, two nonnegative numbers ℓ, u with ℓ≤u and a number δ, the Density Finding Problem is to find the consecutive subsequence A(i ^*,j ^*) over all O(n ²) consecutive subsequences A(i,j) with width constraint satisfying ℓ≤w(i,j)=∑ _r=i ^j w _r ≤u such that its density is closest to δ. The extensively studied Maximum-Density Segment Problem is a special case of the Density Finding Problem with δ=∞. We show that the Density Finding Problem has a lower bound Ω(nlog n) in the algebraic decision tree model of computation. We give an algorithm for the Density Finding Problem that runs in optimal O(nlog n) time and O(nlog n) space for the case when there is no upper bound on the width of the sequence, i.e., u=w(1,n). For the general case, we give an algorithm that runs in O(nlog ² m) time and O(n+mlog m) space, where and w _min=min  _r=1 ⁿ w _r . As a byproduct, we give another O(n) time and space algorithm for the Maximum-Density Segment Problem. Grants NSC95-2221-E-001-016-MY3, NSC-94-2422-H-001-0001, and NSC-95-2752-E-002-005-PAE, and by the Taiwan Information Security Center (TWISC) under the Grants NSC NSC95-2218-E-001-001, NSC95-3114-P-001-002-Y, NSC94-3114-P-001-003-Y and NSC 94-3114-P-011-001.     

Windows scheduling of arbitrary-length jobs on multiple machines

The generalized windows scheduling problem for n jobs on multiple machines is defined as follows: Given is a sequence, I=〈(w ₁,ℓ ₁),(w ₂,ℓ ₂),…,(w _n ,ℓ _n )〉 of n pairs of positive integers that are associated with the jobs 1,2,…,n, respectively. The processing length of job i is ℓ _i slots where a slot is the processing time of one unit of length. The goal is to repeatedly and non-preemptively schedule all the jobs on the fewest possible machines such that the gap (window) between two consecutive beginnings of executions of job i is at most w _i slots. This problem arises in push broadcast systems in which data are transmitted on multiple channels. The problem is NP-hard even for unit-length jobs and a (1+ε)-approximation algorithm is known for this case by approximating the natural lower bound W(I)=?_i=1ⁿ(1/w_i)W(I)=\sum_{i=1}^{n}(1/w_{i}). The techniques used for approximating unit-length jobs cannot be extended for arbitrary-length jobs mainly because the optimal number of machines might be arbitrarily larger than the generalized lower bound W(I)=?_i=1ⁿ(l_i/w_i)W(I)=\sum_{i=1}^{n}(\ell_{i}/w_{i}). The main result of this paper is an 8-approximation algorithm for the WS problem with arbitrary lengths using new methods, different from those used for the unit-length case. The paper also presents another algorithm that uses 2(1+ε)W(I)+logw _max machines and a greedy algorithm that is based on a new tree representation of schedules. The greedy algorithm is optimal for some special cases, and computational experiments show that it performs very well in practice.     

Testing Periodicity

We study the string-property of being periodic and having periodicity smaller than a given bound. Let Σ be a fixed alphabet and let p,n be integers such that p ￡ \fracn2p\leq \frac{n}{2} . A length-n string over Σ, α=(α ₁,…,α _n ), has the property Period(p) if for every i,j∈{1,…,n}, α _i =α _j whenever i≡j (mod p). For an integer parameter g ￡ \fracn2,g\leq \frac{n}{2}, the property Period(≤g) is the property of all strings that are in Period(p) for some p≤g. The property Period( ￡ \fracn2)\mathit{Period}(\leq \frac{n}{2}) is also called Periodicity.     

Spreading of Messages in Random Graphs

Consider the following model on the spreading of messages. A message initially convinces a set of vertices, called the seeds, of the Erdős-Rényi random graph G(n,p). Whenever more than a ρ∈(0,1) fraction of a vertex v’s neighbors are convinced of the message, v will be convinced. The spreading proceeds asynchronously until no more vertices can be convinced. This paper derives lower bounds on the minimum number of initial seeds, min-seed(n,p,d,r)\mathrm{min\hbox{-}seed}(n,p,\delta,\rho), needed to convince a δ∈{1/n,…,n/n} fraction of vertices at the end. In particular, we show that (1) there is a constant β>0 such that min-seed(n,p,d,r)=W(min{d,r}n)\mathrm{min\hbox{-}seed}(n,p,\delta,\rho)=\Omega(\min\{\delta,\rho\}n) with probability 1−n ^−Ω(1) for p≥β (ln (e/min {δ,ρ}))/(ρ n) and (2) min-seed(n,p,d,1/2)=W(dn/ln(e/d))\mathrm{min\hbox{-}seed}(n,p,\delta,1/2)=\Omega(\delta n/\ln(e/\delta)) with probability 1−exp (−Ω(δ n))−n ^−Ω(1) for all p∈[ 0,1 ]. The hidden constants in the Ω notations are independent of p.     

Newton Iterative Parallel Finite Element Algorithm for the Steady Navier-Stokes Equations

A combination method of the Newton iteration and parallel finite element algorithm is applied for solving the steady Navier-Stokes equations under the strong uniqueness condition. This algorithm is motivated by applying the Newton iterations of m times for a nonlinear problem on a coarse grid in domain Ω and computing a linear problem on a fine grid in some subdomains Ω _j ⊂Ω with j=1,…,M in a parallel environment. Then, the error estimation of the Newton iterative parallel finite element solution to the solution of the steady Navier-Stokes equations is analyzed for the large m and small H and h≪H. Finally, some numerical tests are made to demonstrate the the effectiveness of this algorithm.     

Panconnectivity of Cartesian product graphs

A graph G of order n (≥2) is said to be panconnected if for each pair (x,y) of vertices of G there exists an xy-path of length ℓ for each ℓ such that d _G (x,y)≤ℓ≤n−1, where d _G (x,y) denotes the length of a shortest xy-path in G. In this paper, we consider the panconnectivity of Cartesian product graphs. As a consequence of our results, we prove that the n-dimensional generalized hypercube Q _n (k ₁,k ₂,…,k _n ) is panconnected if and only if k _i ≥3 (i=1,…,n), which generalizes a result of Hsieh et al. that the 3-ary n-cube Q³_nQ^{3}_{n} is panconnected.     

Parallel integer sorting and simulation amongst CRCW models

 In this paper a general technique for reducing processors in simulation without any increase in time is described. This results in an O(√log n) time algorithm for simulating one step of PRIORITY on TOLERANT with processor-time product of O(n log log n); the same as that for simulating PRIORITY on ARBITRARY. This is used to obtain an O(log n/log log n+√log n (log log m− log log n)) time algorithm for sorting n integers from the set {0,…, m−1}, m≧n, with a processor-time product of O(n log log m log log n) on a TOLERANT CRCW PRAM. New upper and lower bounds for ordered chaining problem on an allocated COMMON CRCW model are also obtained. The algorithm for ordered chaining takes O(log n/log log n) time on an allocated PRAM of size n. It is shown that this result is best possible (upto a constant multiplicative factor) by obtaining a lower bound of Ω(r log n/(log r+log log n)) for finding the first (leftmost one) live processor on an allocated-COMMON PRAM of size n of r-slow virtual processors (one processor simulates r processors of allocated PRAM). As a result, for ordered chaining problem, “processor-time product” has to be at least Ω(n log n/log log n) for any poly-logarithmic time algorithm. Algorithm for ordered-chaining problem results in an O(log N/log log N) time algorithm for (stable) sorting of n integers from the set {0,…, m−1} with n-processors on a COMMON CRCW PRAM; here N=max(n, m). In particular if, m=n ^O(1) , then sorting takes Θ(log n/log log n) time on both TOLERANT and COMMON CRCW PRAMs. Processor-time product for TOLERANT is O(n(log log n)²). Algorithm for COMMON uses n processors. Received August 13, 1992/June 30, 1995     

Synthesis of logical multiport devices with prescribed dynamics of output processes

The problem of synthesis of a dynamic process in some prescribed form at the output of a logical (n,1)-port device is formulated for a given dependence b = f(a ₁,…, a _n ) of switching the output signal on moments a ₁,…, a _n of switching input signals, where f is a continuous-logic function. A regular procedure is proposed to solve the problem by constructing an (n,1)-port device that implements the required dependence f. Solutions for all possible typical cases and a solution algorithm for the general case are given.     

On the system of word equations x i 1 x i 2…x i m=y i 1 y i 2…y i n (i=1, 2, …) in a free monoid

 It is proved that the system of word equations x ⁱ ₁=y ⁱ ₁ y ⁱ ₂…y ⁱ _n , i=1, 2,…, ⌈n/2⌉ +1, has only cyclic solutions. Some sharpenings concerning the cases n=5, 7 and n≥9 are derived as well as results concerning the general system of equations x ⁱ ₁ x ⁱ ₂…x ⁱ _m =y ⁱ ₁ y ⁱ ₂…y ⁱ _n , i=1, 2,… . Applications to test sets of certain bounded languages are considered. Received: 18 May 1995/2 January 1996     

Surface approximation to scanned data

A method to approximate scanned data points with a B-spline surface is presented. The data are assumed to be organized in the form of Q _i,j, i=0,…,n; j=0,…,m _i, i.e., in a row-wise fashion. The method produces a C ^{(p-1, q-1)} continuous surface (p and q are the required degrees) that does not deviate from the data by more than a user-specified tolerance. The parametrization of the surface is not affected negatively by the distribution of the points in each row, and it can be influenced by a user-supplied knot vector.     

A Fast Algorithm for the Path 2-Packing Problem

Let G be an undirected graph and $\mathcal{T}=\{T_{1},\ldots,T_{k}\}Let G be an undirected graph and T={T₁,?,T_k}\mathcal{T}=\{T_{1},\ldots,T_{k}\} be a collection of disjoint subsets of nodes. Nodes in T ₁∪⋅⋅⋅∪T _k are called terminals, other nodes are called inner. By a T\mathcal{T} -path we mean a path P such that P connects terminals from distinct sets in T\mathcal{T} and all internal nodes of P are inner. We study the problem of finding a maximum cardinality collection ℘ of T\mathcal{T} -paths such that at most two paths in ℘ pass through any node. Our algorithm is purely combinatorial and has the time complexity O(mn ²), where n and m denote the numbers of nodes and edges in G, respectively.     

Quadratic Lower Bound for Permanent Vs. Determinant in any Characteristic

In Valiant’s theory of arithmetic complexity, the classes VP and VNP are analogs of P and NP. A fundamental problem concerning these classes is the Permanent and Determinant Problem: Given a field \mathbbF{\mathbb{F}} of characteristic ≠ 2, and an integer n, what is the minimum m such that the permanent of an n × n matrix X = (x_ij) can be expressed as a determinant of an m × m matrix, where the entries of the determinant matrix are affine linear functions of x_ij ’s, and the equality is in \mathbbF[X]{\mathbb{F}}[{\bf X}]. Mignon and Ressayre (2004) proved a quadratic lower bound m = W(n²)m = \Omega(n^{2}) for fields of characteristic 0. We extend the Mignon–Ressayre quadratic lower bound to all fields of characteristic ≠ 2.     

ℓ 2 2 Spreading Metrics for Vertex Ordering Problems

We design approximation algorithms for the vertex ordering problems Minimum Linear Arrangement, Minimum Containing Interval Graph, and Minimum Storage-Time Product, achieving approximation factors of $O(\sqrt{\log n}\log\log n)We design approximation algorithms for the vertex ordering problems Minimum Linear Arrangement, Minimum Containing Interval Graph, and Minimum Storage-Time Product, achieving approximation factors of O(?{logn}loglogn)O(\sqrt{\log n}\log\log n) , O(?{logn}loglogn)O(\sqrt{\log n}\log\log n) , and O(?{logT}loglogT)O(\sqrt{\log T}\log\log T) , respectively, the last running in time polynomial in T (T being the sum of execution times). The technical contribution of our paper is to introduce “ℓ ₂² spreading metrics” (that can be computed by semidefinite programming) as relaxations for both undirected and directed “permutation metrics,” which are induced by permutations of {1,2,…,n}. The techniques introduced in the recent work of Arora, Rao and Vazirani (Proc. of 36th STOC, pp. 222–231, 2004) can be adapted to exploit the geometry of such ℓ ₂² spreading metrics, giving a powerful tool for the design of divide-and-conquer algorithms. In addition to their applications to approximation algorithms, the study of such ℓ ₂² spreading metrics as relaxations of permutation metrics is interesting in its own right. We show how our results imply that, in a certain sense we make precise, ℓ ₂² spreading metrics approximate permutation metrics on n points to a factor of O(?{logn}loglogn)O(\sqrt{\log n}\log\log n) .     

Oscillatory behavior of delay partial difference equations with positive and negative coefficients

We construct an important transform to obtain sufficient conditions for the oscillation of all solutions of delay partial difference equations with positive and negative coefficients of the form A_m+1,n + A_m,n+1 − A_mn + p_mnA_m−k−1 − q_mnA_{m−k′,n−l′} = 0, where m, n = 0, 1, …, and k, k′, l′, l are nonnegative integers, p, q ϵ (0, ∞), the coefficients {q_mn} and {p_mn} are sequences of nonnegative real numbers.     

Diagonal polynomials and diagonal orders on multidimensional lattices

HereR andN denote the real numbers and the nonnegative integers, respectively. Alsos(x)=x ₁+···+x _n whenx=(x ₁, …,x _n) inR ⁿ. A mapf:R ⁿ →R is call adiagonal function of dimensionn iff|N ⁿ is a bijection ontoN and, for allx, y inN ⁿ, f(x)<f(y) whens(x)<s(y). Morales and Lew [6] constructed 2 ⁿ⁻² inequivalent diagonal polynomial functions of dimensionn for eachn>1. Here we use new combinatorial ideas to show that numberd _n of such functions is much greater than 2 ⁿ⁻² forn>3. These combinatorial ideas also give an inductive procedure to constructd _n+1 diagonal orderings of {1, …,n}.