Paths and trails in edge-colored graphs

This paper deals with the existence and search for properly edge-colored paths/trails between two, not necessarily distinct, vertices s$s$ and t$t$ in an edge-colored graph from an algorithmic perspective. First we show that several versions of the s−t$s-t$ path/trail problem have polynomial solutions including the shortest path/trail case. We give polynomial algorithms for finding a longest properly edge-colored path/trail between s$s$ and t$t$ for a particular class of graphs and characterize edge-colored graphs without properly edge-colored closed trails. Next, we prove that deciding whether there exist k$k$ pairwise vertex/edge disjoint properly edge-colored s−t$s-t$ paths/trails in a c$c$-edge-colored graph G^c$G^c$ is NP-complete even for k=2$k=2$ and c=Ω(n²)$c=\Omega \left(n^2\right)$, where n$n$ denotes the number of vertices in G^c$G^c$. Moreover, we prove that these problems remain NP-complete for c$c$-edge-colored graphs containing no properly edge-colored cycles and c=Ω(n)$c=\Omega \left(n\right)$. We obtain some approximation results for those maximization problems together with polynomial results for some particular classes of edge-colored graphs.     

Spanners for bounded tree-length graphs

This paper concerns construction of additive stretched spanners with few edges for n$n$-vertex graphs having a tree-decomposition into bags of diameter at most δ$\delta$, i.e., the tree-length δ$\delta$ graphs. For such graphs we construct additive 2δ$2\delta$-spanners with O(δn+nlogn)$O(\delta n+nlogn)$ edges, and additive 4δ$4\delta$-spanners with O(δn)$O\left(\delta n\right)$ edges. This provides new upper bounds for chordal graphs for which δ=1$\delta =1$. We also show a lower bound, and prove that there are graphs of tree-length δ$\delta$ for which every multiplicative δ$\delta$-spanner (and thus every additive (δ−1)$(\delta -1)$-spanner) requires Ω(n^1+1/Θ(δ))$\Omega \left(n^{1+1/\Theta \left(\delta \right)}\right)$ edges.     

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$h$.     

Swapping a failing edge of a shortest paths tree by minimizing the average stretch factor

We consider a two-edge connected, undirected graph G=(V,E)$G=(V,E)$, with n$n$ nodes and m$m$ non-negatively real weighted edges, and a single source shortest paths tree (SPT) T$T$ of G$G$ rooted at an arbitrary node r$r$. If an edge in T$T$ is temporarily removed, it makes sense to reconnect the nodes disconnected from the root by adding a single non-tree edge, called a swap edge  , instead of rebuilding a new optimal SPT from scratch. In the past, several optimality criteria have been considered to select a best possible swap edge. In this paper we focus on the most prominent one, that is the minimization of the average distance between the root and the disconnected nodes. To this respect, we present an O(mlog²n)$O\left(m{log}^{2}n\right)$ time and O(m)$O\left(m\right)$ space algorithm to find a best swap edge for every edge of T$T$, thus improving for m=o(n²/log²n)$m=o(n^2/{log}^{2}n)$ the previously known O(n²)$O\left(n^2\right)$ time and space complexity algorithm.     

A new self-stabilizing maximal matching algorithm

The maximal matching problem has received considerable attention in the self-stabilizing community. Previous work has given several self-stabilizing algorithms that solve the problem for both the adversarial and the fair distributed daemon, the sequential adversarial daemon, as well as the synchronous daemon. In the following we present a single self-stabilizing algorithm for this problem that unites all of these algorithms in that it has the same time complexity as the previous best algorithms for the sequential adversarial, the distributed fair, and the synchronous daemon. In addition, the algorithm improves the previous best time complexities for the distributed adversarial daemon from O(n²)$O\left(n^2\right)$ and O(δm)$O\left(\delta m\right)$ to O(m)$O\left(m\right)$ where n$n$ is the number of processes, m$m$ is the number of edges, and δ$\delta$ is the maximum degree in the graph.     

Optimal lower bounds for rank and select indexes

We develop a new lower bound technique for data structures. We show an optimal Ω(nlglgn/lgn)$\Omega (nlglgn/lgn)$ space lower bounds for storing an index that allows to implement rank and select queries on a bit vector B$B$ provided that B$B$ is stored explicitly. These results improve upon [Peter Bro Miltersen, Lower bounds on the size of selection and rank indexes, in: Proceedings of the 16th Annual ACM–SIAM Symposium on Discrete Algorithms, 2005, pp. 11–12]. We show Ω((m/t)lgt)$\Omega ((m/t)lgt)$ lower bounds for storing rank/select index in the case where B$B$ has m$m$ 1$1$-bits in it and the algorithm is allowed to probe t$t$ bits of B$B$. We also present an improved data structure that implements both rank and select queries with an index of size (1+o(1))(nlglgn/lgn)+O(n/lgn)$(1+o\left(1\right))(nlglgn/lgn)+O(n/lgn)$, that is, compared to existing results we give an explicit constant for storage in the RAM model with word size lgn$lgn$. An advantage of this data structure is that both rank and select indexes share the most space consuming part of order Θ(nlglgn/lgn)$\Theta (nlglgn/lgn)$ making it more practical for implementation.     

Faster algorithms for computing Hong’s bound on absolute positiveness

We show how to compute Hong’s bound for the absolute positiveness of a polynomial in d$d$ variables with maximum degree δ$\delta$ in O(nlog^dn)$O\left(n{log}^{d}n\right)$ time, where n$n$ is the number of non-zero coefficients. For the univariate case, we give a linear time algorithm. As a consequence, the time bounds for the continued fraction algorithm for real root isolation improve by a factor of δ$\delta$.     

Expanders and time-restricted branching programs

The replication number   of a branching program is the minimum number R$R$ such that along every accepting computation at most R$R$ variables are tested more than once; the sets of variables re-tested along different computations may be different. For every branching program, this number lies between 0$0$ (read-once programs) and the total number n$n$ of variables (general branching programs). The best results so far were exponential lower bounds on the size of branching programs with R=o(n/logn)$R=o(n/logn)$. We improve this to R≤?n$R\le ?n$ for a constant ?>0$?>0$. This also gives an alternative and simpler proof of an exponential lower bound for (1+?)n$(1+?)n$ time branching programs for a constant ?>0$?>0$. We prove these lower bounds for quadratic functions of Ramanujan graphs.     

Large independent sets in random regular graphs

We present algorithmic lower bounds on the size _sd$s_d$ of the largest independent sets of vertices in random d$d$-regular graphs, for each fixed d≥3$d\ge 3$. For instance, for d=3$d=3$ we prove that, for graphs on n$n$ vertices, _sd≥0.43475n$s_d\ge 0.43475n$ with probability approaching one as n$n$ tends to infinity.     

Algorithms for near solutions to polynomial equations

Let F(x,y)$F(x,y)$ be a polynomial over a field K$K$ and m$m$ a nonnegative integer. We call a polynomial g$g$ over K$K$ an m$m$-near solution of F(x,y)$F(x,y)$ if there exists a c∈K$c\in K$ such that F(x,g)=cx^m$F(x,g)=c{x}^m$, and the number c$c$ is called an m$m$-value of F(x,y)$F(x,y)$ corresponding to g$g$. In particular, c$c$ can be 0. Hence, by viewing F(x,y)=0$F(x,y)=0$ as a polynomial equation over K[x]$K\left[x\right]$ with variable y$y$, every solution of the equation F(x,y)=0$F(x,y)=0$ in K[x]$K\left[x\right]$ is also an m$m$-near solution. We provide an algorithm that gives all m$m$-near solutions of a given polynomial F(x,y)$F(x,y)$ over K$K$, and this algorithm is polynomial time reducible to solving one variable equations over K$K$. We introduce approximate solutions to analyze the algorithm. We also give some interesting properties of approximate solutions.     

The cell probe complexity of succinct data structures

We consider time-space tradeoffs for static data structure problems in the cell probe model with word size 1 (the bit probe model). In this model, the goal is to represent n$n$-bit data with s=n+r$s=n+r$ bits such that queries (of a certain type) about the data can be answered by reading at most t$t$ bits of the representation. Ideally, we would like to keep both s$s$ and t$t$ small, but there are tradeoffs between the values of s$s$ and t$t$ that limit the possibilities of keeping both parameters small.     

Origami fold as algebraic graph rewriting

We formalize paper fold (origami) by graph rewriting. Origami construction is abstractly described by a rewriting system (O,?)$(\mathbb{O},?)$, where O$\mathbb{O}$ is the set of abstract origamis and ?$?$ is a binary relation on O$\mathbb{O}$, that models fold  . An abstract origami is a structure (Π,∽,?)$(\Pi ,\backsim ,?)$, where Π$\Pi$ is a set of faces constituting an origami, and ∽$\backsim$ and ?$?$ are binary relations on Π$\Pi$, each representing adjacency and superposition relations between the faces.     

Conjunctive query evaluation by search-tree revisited

The most natural and perhaps most frequently used method for testing membership of an individual tuple in a conjunctive query is based on searching trees of partial solutions, or search-trees. We investigate the question of evaluating conjunctive queries with a time-bound guarantee that is measured as a function of the size of the optimal search-tree. We provide an algorithm that, given a database D$D$, a conjunctive query Q$Q$, and a tuple a$a$, tests whether Q(a)$Q\left(a\right)$ holds in D$D$ in time bounded by a polynomial in (sn)^logk(sn)^loglogn${\left(sn\right)}^{logk}{\left(sn\right)}^{loglogn}$ and n^r$n^r$, where n$n$ is the size of the domain of the database, k$k$ is the number of bound variables of the conjunctive query, s$s$ is the size of the optimal search-tree, and r$r$ is the maximum arity of the relations. In many cases of interest, this bound is significantly smaller than the n^O(k)$n^{O\left(k\right)}$ bound provided by the naive search-tree method. Moreover, our algorithm has the advantage of guaranteeing the bound for any given conjunctive query. In particular, it guarantees the bound for queries that admit an equivalent form that is much easier to evaluate, even when finding such a form is an NP-hard task. Concrete examples include the conjunctive queries that can be non-trivially folded into a conjunctive query of bounded size or bounded treewidth. All our results translate to the context of constraint-satisfaction problems via the well-publicized correspondence between both frameworks.