Divergence bounded computable real numbers

A real x$x$ is called h$h$-bounded computable  , for some function h:N→N$h:\mathbb{N}\to \mathbb{N}$, if there is a computable sequence (_xs)$(x_s)$ of rational numbers which converges to x$x$ such that, for any n∈N$n\in \mathbb{N}$, at most h(n)$h(n)$ non-overlapping pairs of its members are separated by a distance larger than 2^-n$2^{-n}$. In this paper we discuss properties of h$h$-bounded computable reals for various functions h$h$. We will show a simple sufficient condition for a class of functions h$h$ such that the corresponding h$h$-bounded computable reals form an algebraic field. A hierarchy theorem for h$h$-bounded computable reals is also shown. Besides we compare semi-computability and weak computability with the h$h$-bounded computability for special functions h$h$.     

Efficient detection of a locally stable predicate in a distributed system

We present an efficient approach to detect a locally stable predicate in a distributed computation. Examples of properties that can be formulated as locally stable predicates include termination and deadlock of a subset of processes. Our algorithm does not require application messages to be modified to carry control information (e.g., vector timestamps), nor does it inhibit events (or actions) of the underlying computation. The worst-case message complexity of our algorithm is O(n(m+1))$O(n(m+1))$, where n$n$ is the number of processes in the system and m$m$ is the number of events executed by the underlying computation. We show that, in practice, its message complexity should be much lower than its worst-case message complexity. The detection latency of our algorithm is O(d)$O(d)$ time units, where d$d$ is the diameter of communication topology. Our approach also unifies several known algorithms for detecting termination and deadlock. We also show that our algorithm for detecting a locally stable predicate can be used to efficiently detect a stable predicate that is a monotonic function of other locally stable predicates.     

A concurrent lambda calculus with futures

We introduce a new lambda calculus with futures, λ(fut)$\lambda (\mathsf{fut})$, that models the operational semantics of concurrent statically typed functional programming languages with mixed eager and lazy threads such as Alice ML, a concurrent extension of Standard ML. λ(fut)$\lambda (\mathsf{fut})$ is a minimalist extension of the call-by-value λ$\lambda$-calculus that is sufficiently expressive to define and combine a variety of standard concurrency abstractions, such as channels, semaphores, and ports. Despite its minimality, the basic machinery of λ(fut)$\lambda (\mathsf{fut})$ is sufficiently powerful to support explicit recursion and call-by-need evaluation.     

On hypercube packings,blocking sets and a covering problem

Consider sets S of hypercubes of side 2 in the discrete n-dimensional torus of side 4 with the property that every possible hypercube of side 2 has a nonempty intersection with some hypercube in S. The problem of minimizing the size of S is studied in two settings, depending on whether intersections between hypercubes in S are allowed or not. If intersections are not allowed, then one is asking for the smallest size of a non-extensible packing S  ; this size is denoted by f(n)$f(n)$. If intersections are allowed, then the structure S is called a blocking set. The smallest size of a blocking set S   is denoted by h(n)$h(n)$. By computer-aided techniques, it is shown that f(5)=12$f(5)=12$, f(6)=16$f(6)=16$, h(6)=15$h(6)=15$ and h(7)≤23$h(7)\le 23$. Also, non-extensible packings as well as blocking sets of certain small sizes are classified for n≤6$n\le 6$. There is a direct connection between these problems and a covering problem originating from the football pools.     

Compatibility of unrooted phylogenetic trees is FPT

A collection of _T1,_T2,…,_Tk$T_1,T_2,\dots ,T_k$ of unrooted, leaf labelled (phylogenetic) trees, all with different leaf sets, is said to be compatible   if there exists a tree T$T$ such that each tree _Ti$T_i$ can be obtained from T$T$ by deleting leaves and contracting edges. Determining compatibility is NP-hard, and the fastest algorithm to date has worst case complexity of around Ω(n^k)$\mathrm{\Omega }(n^k)$ time, n$n$ being the number of leaves. Here, we present an O(nf(k))$\mathrm{O}(\mathit{nf}(k))$ algorithm, proving that compatibility of unrooted phylogenetic trees is fixed parameter tractable   (FPT) with respect to the number k$k$ of trees.     

A self-stabilizing algorithm to maximal 2-packing with improved complexity

In self-stabilization, each node has a local view of the distributed network system, in a finite amount of time the system converges to a global setup with desired property, in this case establishing a 2-packing set. Using a graph G=(V,E)$G=(V,E)$ to represent the network, a subset S⊆V$S\subseteq V$ is a 2-packing if ∀i∈V:|N[i]∩S|?1$\forall i\in V:|N[i]\cap S|?1$. In this paper, we first propose an ID-based, constant space, self-stabilizing algorithm that stabilizes to a maximal 2-packing in an arbitrary graph. We show that the algorithm stabilizes in O(mn)$O(mn)$ moves under any scheduler (such as a distributed daemon). Secondly, we show that the algorithm stabilizes in O(n²)$O(n^2)$ rounds under a synchronous daemon where every privileged node moves at each round.     

Optimum sweeps of simple polygons with two guards

A polygon P admits a sweep if two mobile guards can detect an unpredictable, moving target inside P  , no matter how fast the target moves. Two guards move on the polygon boundary and are required to always be mutually visible. The objective of this study is to find an optimum sweep such that the sum of the distances travelled by the two guards in the sweep is minimized. We present an O(n²)$O(n^2)$ time and O(n)$O(n)$ space algorithm for optimizing this metric, where n   is the number of vertices of the given polygon. Our result is obtained by reducing this problem to finding a shortest path between two nodes in a graph of size O(n)$O(n)$.     

The complexity of the zero-sum 3-flows

A zero-sum k-flow for a graph G   is a vector in the null-space of the 0,1$0,1$-incidence matrix of G   such that its entries belong to {±1,?,±(k−1)}$\{\pm 1,?,\pm (k-1)\}$. Akbari et al. (2009) [5] conjectured that if G is a graph with a zero-sum flow, then G   admits a zero-sum 6-flow. (2,3)$(2,3)$-semiregular graphs are an important family in studying zero-sum flows. Akbari et al. (2009) [5] proved that if Zero-Sum Conjecture is true for any (2,3)$(2,3)$-semiregular graph, then it is true for any graph. In this paper, we show that there is a polynomial time algorithm to determine whether a given (2,3)$(2,3)$-graph G   has a zero-sum 3-flow. In fact, we show that, there is a polynomial time algorithm to determine whether a given (2,4)$(2,4)$-graph G with n   vertices has a zero-sum 3-flow, where the number of vertices of degree four is O(log?n)$O(\log ?n)$. Furthermore, we show that it is NP-complete to determine whether a given (3,4)$(3,4)$-semiregular graph has a zero-sum 3-flow.     

Choosing starting values for certain Newton–Raphson iterations

We aim at finding the best possible seed values when computing a^1/p$a^{1/p}$ using the Newton–Raphson iteration in a given interval. A natural choice of the seed value would be the one that best approximates the expected result. It turns out that in most cases, the best seed value can be quite far from this natural choice. When we evaluate a monotone function f(a)$f(a)$ in the interval [_amin,_amax]$[a_{\min },a_{\max }]$, by building the sequence _xn$x_n$ defined by the Newton–Raphson iteration, the natural choice consists in choosing _x0$x_0$ equal to the arithmetic mean of the endpoint values. This minimizes the maximum possible distance between _x0$x_0$ and f(a)$f(a)$. And yet, if we perform n$n$ iterations, what matters is to minimize the maximum possible distance between _xn$x_n$ and f(a)$f(a)$. In several examples, the value of the best starting point varies rather significantly with the number of iterations.     

Practical perfect hashing in nearly optimal space

A hash function is a mapping from a key universe U   to a range of integers, i.e., h:U?{0,1,…,m−1}$h:U?\{\mathrm{0,1},\dots ,m-1\}$, where m is the range's size. A perfect hash function   for some set S⊆U$S\subseteq U$ is a hash function that is one-to-one on S  , where m≥|S|$m\ge \left|S\right|$. A minimal perfect hash function   for some set S⊆U$S\subseteq U$ is a perfect hash function with a range of minimum size, i.e., m=|S|$m=\left|S\right|$. This paper presents a construction for (minimal) perfect hash functions that combines theoretical analysis, practical performance, expected linear construction time and nearly optimal space consumption for the data structure. For n keys and m=n   the space consumption ranges from 2.62n+o(n)$2.62n+o(n)$ to 3.3n+o(n)$3.3n+o(n)$ bits, and for m=1.23n$m=1.23n$ it ranges from 1.95n+o(n)$1.95n+o(n)$ to 2.7n+o(n)$2.7n+o(n)$ bits. This is within a small constant factor from the theoretical lower bounds of 1.44n$1.44n$ bits for m=n   and 0.89n$0.89n$ bits for m=1.23n$m=1.23n$. We combine several theoretical results into a practical solution that has turned perfect hashing into a very compact data structure to solve the membership problem when the key set S is static and known in advance. By taking into account the memory hierarchy we can construct (minimal) perfect hash functions for over a billion keys in 46 min using a commodity PC. An open source implementation of the algorithms is available at http://cmph.sf.net under the GNU Lesser General Public License (LGPL).     

Faster two-dimensional pattern matching with rotations

The most efficient currently known algorithms for two-dimensional pattern matching with rotations have a worst case time complexity of O(n²m³)$\mathrm{O}(n^2{m}^3)$, where the size of the text is n×n$n\times n$ and the size of the pattern is m×m$m\times m$. In this paper we present a new algorithm for the problem whose running time is O(n²m²)$\mathrm{O}(n^2{m}^2)$.     

On miniaturized problems in parameterized complexity theory

We introduce a general notion of miniaturization of a problem that comprises the different miniaturizations of concrete problems considered so far. We develop parts of the basic theory of miniaturizations. Using the appropriate logical formalism, we show that the miniaturization of a definable problem in W[t]$\mathrm{W}[t]$ lies in W[t]$\mathrm{W}[t]$, too. In particular, the miniaturization of the dominating set problem is in W[2]$\mathrm{W}[2]$. Furthermore, we investigate the relation between f(k)·n^o(k)$f(k)·n^{\mathrm{o}(k)}$ time and subexponential time algorithms for the dominating set problem and for the clique problem.     

First steps to the runtime complexity analysis of ant colony optimization

The paper presents results on the runtime complexity of two ant colony optimization (ACO) algorithms: ant system, the oldest ACO variant, and GBAS, the first ACO variant for which theoretical convergence results have been established. In both cases, as the class of test problems under consideration, a slight generalization of the well-known OneMax test function has been chosen. The techniques used for the runtime analysis of the two algorithms differ: in the case of GBAS, the expected runtime until the optimal solution is reached is studied by a direct bound estimation approach inspired by comparable results for the (1+1)$(1+1)$ evolutionary algorithm (EA). A runtime bound of order O(mlogm)$O(m\log m)$, where m   is the problem instance size, is obtained. In the case of ant system, the original discrete stochastic process is approximated by a suitable continuous deterministic process. The validity of the approximation is shown by means of a rigid convergence theorem exploiting a classical result from mathematical learning theory. Using this approximation, it is demonstrated that for the considered OneMax-type problems, a runtime of order O(mlog(1/ε))$O(m\log (1/\epsilon ))$ until reaching an expected relative   solution quality of 1-ε$1-\epsilon$, and a runtime of O(mlogm)$O(m\log m)$ until reaching the optimal   solution with high probability can be predicted. Our results are the first to show competitiveness in runtime complexity with (1+1$1+1$) EA on OneMax for a proper ACO algorithm.     

An improved algorithm for the p-center problem on interval graphs with unit lengths

The p-center problem is to locate p facilities in a network of n   demand points so as to minimize the longest distance between a demand point and its nearest facility. We consider this problem by modelling the network as an interval graph whose edges all have unit lengths. We present an O(n)$O(n)$ time algorithm for the problem under the assumption that the endpoints of the intervals are sorted, which improves on the existing best algorithm for the problem that has a run time of O(pn)$O(\mathit{pn})$.     

A polynomial-time algorithm for computing K-terminal residual reliability of d-trapezoid graphs

Consider a probabilistic graph G   in which the edges of E(G)$E(G)$ are perfectly reliable, but the vertices of V(G)$V(G)$ may fail with some known probabilities. Given a subset K   of V(G)$V(G)$, the K-terminal residual reliability of G is the probability that all operational vertices in K are connected to each other. This problem can be considered to be a generalization of two well-known reliability problems – the K-terminal reliability problem and the residual connectedness reliability problem.     

Computing phylogenetic roots with bounded degrees and errors is NP-complete

In this paper we study the computational complexity of the following optimization problem: given a graph G=(V,E)$G=(V,E)$, we wish to find a tree T such that (1) the degree of each internal node of T   is at least 3 and at most Δ$\Delta$, (2) the leaves of T are exactly the elements of V, and (3) the number of errors, that is, the symmetric difference between E   and {{u,v}:u,v$\{\{u,v\}:u,v$ are leaves of T   and _dT(u,v)≤k}$d_T(u,v)\le k\}$, is as small as possible, where _dT(u,v)$d_T(u,v)$ denotes the distance between u$u$ and v$v$ in tree T  . We show that this problem is NP-hard for all fixed constants Δ,k≥3$\Delta ,k\ge 3$.     

A simple optimal representation for balanced parentheses

We consider succinct, or highly space-efficient, representations of a (static) string consisting of n   pairs of balanced parentheses, which support natural operations such as finding the matching parenthesis for a given parenthesis, or finding the pair of parentheses that most tightly enclose a given pair. This problem was considered by Jacobson [Space-efficient static trees and graphs, in: Proc. of the 30th FOCS, 1989, pp. 549–554] and Munro and Raman [Succinct representation of balanced parentheses and static trees, SIAM J. Comput. 31 (2001) 762–776] who gave O(n)$\mathrm{O}(n)$-bit and 2n+o(n)$2n+\mathrm{o}(n)$-bit representations, respectively, that supported the above operations in O(1)$\mathrm{O}(1)$ time on the RAM model of computation. This data structure is a fundamental tool in succinct representations, and has applications in representing suffix trees, ordinal trees, planar graphs and permutations.