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Branchings play an important role in digraph theory and algorithms. In particular, a chapter in the monograph of Bang-Jensen and Gutin, Digraphs: Theory, Algorithms and Application, Ed. 2, 2009 is wholly devoted to branchings. The well-known Edmonds Branching Theorem provides a characterization for the existence of k arc-disjoint out-branchings rooted at the same vertex. A short proof of the theorem by Lovász (1976) leads to a polynomial-time algorithm for finding such out-branchings. A natural related problem is to characterize digraphs having an out-branching and an in-branching which are arc-disjoint. Such a pair of branchings is called a good pair. Bang-Jensen, Bessy, Havet and Yeo (2022) pointed out that it is NP-complete to decide if a given digraph has a good pair. They also showed that every digraph of independence number at most 2 and arc-connectivity at least 2 has a good pair, which settled a conjecture of Thomassen for digraphs of independence number 2. Then they asked for the smallest number n n g p of vertices in a 2-arc-strong digraph which has no good pair. They proved that 7 ≤ n n g p ≤ 10. In this paper, we prove that n n g p = 10 , which solves the open problem. • The study of good pair has relations with Tutte's Packing Theorem and Edmonds Branching Theorem. • We answer a problem by Bang-Jensen, Bessy, Havet and Yeo about the minimum order of 2-arc-strong digraphs without good pairs. • We study the relationship between Hamilton dipath and good pair. • We generalize some results by Bang-Jensen, Bessy, Havet and Yeo on good pair. [ABSTRACT FROM AUTHOR]

We consider the problem of computing the diameter of a unicycle graph (i.e., a graph with a unique cycle). We present an O (n) time algorithm for the problem, where n is the number of vertices of the graph. This improves the previous best O (n log ⁡ n) time solution [Oh and Ahn, ISAAC 2016]. Using this algorithm as a subroutine, we solve the problem of adding a shortcut to a tree so that the diameter of the new graph (which is a unicycle graph) is minimized; our algorithm takes O (n 2 log ⁡ n) time and O (n) space. The previous best algorithms solve the problem in O (n 2 log 3 ⁡ n) time and O (n) space [Oh and Ahn, ISAAC 2016], or in O (n 2) time and O (n 2) space [Bilò, ISAAC 2018]. • The paper presents a linear time algorithm for computing the diameter of unicycle graphs. • The paper gives a new algorithm which finds a new edge to add to a tree so that the diameter of the new graph is minimized. • The algorithms rely on certain domination relations among edges of the graphs. [ABSTRACT FROM AUTHOR]

We consider the problem of augmenting an n -vertex tree with one shortcut in order to minimize the diameter of the resulting graph. The tree is embedded in an unknown space and we have access to an oracle that, when queried on a pair of vertices u and v , reports the weight of the shortcut (u , v) in constant time. Previously, the problem was solved in O (n 2 log 3 ⁡ n) time for general weights [Oh and Ahn, ISAAC 2016], in O (n 2 log ⁡ n) time for trees embedded in a metric space [Große et al. , Int. J. FCS], and in O (n log ⁡ n) time for paths embedded in a metric space [Wang, Comput. Geom.]. Very recently an algorithm for general weights requiring O (n 2 log ⁡ n) time and O (n) space has been developed [Wang and Zhao, Theor. Comp. Science]. Finally, a (1 + ε) -approximation algorithm running in O (n + 1 / ε 3) has been designed for paths embedded in R d , for constant values of d [Große et al. , Int. J. FCS]. In this paper we design an asymptotic time-optimal algorithm for trees and general edge-weights that requires O (n 2) time and O (n log ⁡ n) space. Moreover, for trees embedded in a metric space, we design (i) an exact O (n log ⁡ n) -time algorithm and (ii) a (1 + ε) -approximation algorithm that runs in O (n + ε − 1 log ⁡ ε − 1) time. [ABSTRACT FROM AUTHOR]

Motivated by the design of cost-efficient communication networks, the problem about Maximum Internal Spanning Tree that arises in a connected graph, is proposed to find a spanning tree with the maximum number of internal vertices. In 2018, Xingfu Li et al. presented a polynomial algorithm to find a maximum internal spanning tree in a connected interval graph. Based on the structure of normal orderings on interval graphs, we present a simple linear time algorithm that solve the problem when restricted to connected interval graphs in this paper. The proof provides additional insight about the linear time algorithm on interval graphs. • The MIST problem is solvable in a simple linear time algorithm under the restriction of connected interval graphs. • The structure of normal orderings on interval graphs is proposed to solve the Maximum Internal Spanning Tree problem. • The proof of the main results is more easy to understand with proper cases to prove. [ABSTRACT FROM AUTHOR]

In this paper, we revisit the problem of computing longest common almost increasing subsequence (LCAIS) where, given two input sequences, the goal is to compute a common subsequence that is 'almost' increasing. Here the concept of an almost increasing subsequence offers an interesting relaxation over the increasing condition. This problem has been studied in the literature albeit with some constraints. Here, we present a number of a number of algorithmic approaches to solve the problem more generally and efficiently. [ABSTRACT FROM AUTHOR]

A new algorithm is proposed in this paper for computing the nearest polynomial to multiple given polynomials with a given zero in the real case, where the distance between polynomials is defined by the weighted ℓ 2 , q norm (0 < q ≤ 2). First, the problem is formulated as a univariate constrained non-convex minimization problem, where prior information of the coefficients of the polynomial can be embedded by selecting proper weights. Then, an iteratively reweighted algorithm is designed to solve the obtained problem, and also the convergence and rate of convergence are uniformly demonstrated for all q in (0 , 2 ]. Since all the existing methods for computing the nearest polynomial to multiple given polynomials with a given zero are limited to the real case, we ingeniously extend the results to the complex case. Finally, two representative examples that separately compute the nearest real and complex polynomials are presented to show the effectiveness of the proposed algorithm. • A weighted ℓ 2 , q -norm based minimization problem is constructed with prior information of polynomial embedded. • After converting the problem into a univariate problem, an iteratively reweighted algorithm is proposed to solve it. • Using properties of a concave function, convergence and rate of convergence of the proposed algorithm are provided. • It is the first time to study computation of the nearest polynomial to multiple given polynomials with a given zero in the complex case. [ABSTRACT FROM AUTHOR]

The longest common palindromic subsequence (LCPS) problem is a variant of the longest common subsequence (LCS) problem. Given two input sequences A and B , the LCPS problem is to find the common subsequence of A and B such that the answer is a palindrome with the maximal length. The LCPS problem was first proposed by Chowdhury et al. (2014) [9] , who proposed a dynamic programming algorithm with O (m 2 n 2) time, where m and n denote the lengths of A and B , respectively. This paper proposes a diagonal-based algorithm for solving the LCPS problem with O (L (m − L) R log ⁡ n) time and O (R L) space, where R denotes the number of match pairs between A and B , and L denotes the LCPS length. In our algorithm, the 3-dimensional minima finding algorithm is invoked to overcome the domination problem. As experimental results show, our algorithm is efficient practically compared with some previously published algorithms. [ABSTRACT FROM AUTHOR]

This article explores an interplay between process crash failures and concurrency. Namely, it aims at answering the question, "Is it possible to cope with more crash failures when some number of crashes occur before some predefined contention point happened?". These crashes are named λ-constrained crashes , where λ is the predefined contention point (known by the processes). Hence, this article considers two types of process crashes: λ -constrained crashes and classical crashes (which can occur at any time and are consequently called any-time crashes). Considering a system made up of n asynchronous processes communicating through atomic read/write registers, the article focuses on the design of two agreement-related algorithms. Assuming λ = n − 1 and no any-time failure, the first algorithm solves the consensus problem in the presence of one λ -constrained crash failure, thereby circumventing the well-known FLP impossibility result. The second algorithm considers k -set agreement for k ≥ 2. It is a k -set agreement algorithm such that λ = n − ℓ and ℓ ≥ k = m + f that works in the presence of up to (2 m + ℓ − k) λ -constrained crashes and (f − 1) any-time crashes, i.e., up to t = (2 m + ℓ − k) + (f − 1) process crashes. It follows that considering the timing of failures with respect to a predefined contention point enlarges the space of executions in which k -set agreement can be solved despite the combined effect of asynchrony, concurrency, and process crashes. The paper also presents agreement-related impossibility results for consensus and k -set agreement in the context of λ -constrained process crashes (with or without any-time crashes). [ABSTRACT FROM AUTHOR]

The Minimum Vertex Cover P roblem is the optimization problem of finding a vertex cover V c of minimal cardinality in a given graph. It is a classic NP-hard problem, and various algorithms have been suggested for it. In this paper, we start with a basic algorithm for solving the problem. Using a probabilistic idea, we use it to develop an improved algorithm. The algorithm is greedy; at each step it adds to the cover a vertex such that the expected cover size, if we continue randomly after this step, is minimal. We study the new algorithm theoretically and empirically, and run simulations to compare its performance to that of some algorithms of a similar nature. [ABSTRACT FROM AUTHOR]

The problem of computing a connected network with minimum interference is a fundamental problem in wireless sensor networks. Several models of interference have been studied in the literature. The most common one is the receiver-centric, in which the interference of a node p is defined as the number of other nodes whose transmission range covers p. In this paper, we study the problem of assigning a transmission range to each sensor, such that the resulting network is strongly connected and the total interference of the network is minimized. For the one-dimensional case, we show how to solve the problem optimally in O (n 3) time. For the two-dimensional case, we show that the problem is NP-complete and give a polynomial-time 2-approximation algorithm for the problem. [ABSTRACT FROM AUTHOR]

Consider n identical relocatable sensors , with sensing range r and visibility range 2 r , initially placed at arbitrary positions on a line segment barrier; each sensor can detect the presence of an intruder in its sensing range, and is said to cover the portion of the barrier that intersects with its sensing range. Sensors operate in Look-Compute-Move cycles: in a cycle a sensor determines the positions of sensors in its visibility range, it computes its next position (within its visibility range), and then moves to the calculated position. The barrier coverage problem we consider is for the sensors to independently make decisions and movements so to reach final positions whereby they collectively cover the barrier; in particular we are interested in oblivious (or memoryless) sensors, and focus on the impact that the level of synchrony and the presence/absence of orientation (i.e., global notion of "left-right") have on the solvability of the problem. It is known that without orientation, oblivious sensors can solve the problem if they are fully synchronous (i.e., they operate in synchronous rounds and are all active at every round). In this paper, we prove that orientation is critical to being able to solve the problem if we relax the assumption of full synchronization. We first show that if sensors are unoriented, then barrier coverage is unsolvable even in the semi-synchronous setting. In contrast, if sensors agree on a global orientation, then we give an algorithm for barrier coverage, even in the completely asynchronous setting. Finally, we extend the existing result of Cohen and Peleg and show that convergence to barrier coverage by unoriented sensors in the semi-synchronous model is possible with bounded visibility range 2 r + ρ (for arbitrarily small ρ > 0) and bounded mobility range r. [ABSTRACT FROM AUTHOR]

Given two sequences A and B of lengths m and n , respectively, the consecutive suffix alignment (CSA) problem is to compute the longest common subsequence (LCS) between A and each suffix of B. The data structure of two-dimensional matrix for solving the CSA problem is named S-table. The S-table would be infeasible due to its quadratic-space requirement for long sequences. The linear-space S-table, proposed by Alves et al. (2005), consists of the first row of the S-table and the changes between every two consecutive rows. However, there is no further discussions and practical applications for the linear-space S-table. This paper proposes algorithms for improving three problems, related to LCS and S-table, by using the linear-space S-table. Suppose that A = A (1) A (2) (concatenation of two substrings), and we are given the S-table of A (2) and B , as well as the alignment result (LCS length) of A (1) to each prefix of B. The concatenated LCS (CoLCS) problem is to find the alignment result of A and B. For the CoLCS problem, this paper proposes an O(n)-time algorithm with the technique of set union-find to achieve the linear space. Next, for merging two linear S-tables, we propose an O (n log ⁡ n) -time algorithm with the technique of range query and update to improve the quadratic-time algorithm using quadratic-space S-tables. Then, we propose an O(n)-time algorithm to update the linear-space S-table of A and B to obtain the linear-space S-table of Aσ and B , where σ denotes a new character appended to the tail of A. For further applications to long sequences by using the linear-space S-table, the algorithms proposed in this paper are essential and necessary. [ABSTRACT FROM AUTHOR]

A 2-club is a graph of diameter at most two. In the decision version of the 2-Club Cluster Edge Deletion problem, an undirected graph G is given along with an integer k≥0 as parameter, and the question is whether G can be transformed into a disjoint union of 2-clubs by deleting at most k edges. A simple fixed-parameter algorithm solves the problem in O ⁎ (3 k) , and a decade-old algorithm improved this running to O ⁎ (2.74 k) time via a more sophisticated case analysis. Unfortunately, this latter algorithm is shown to have a flawed branching scenario. A fixed-parameter algorithm that breaks the 3 k barrier is presented in this paper, with a running time in O ⁎ (2.692 k). This also improves the previously claimed running time. [ABSTRACT FROM AUTHOR]

A simple Voting algorithm has been shown to be effective at solving the OneMax problem in the presence of high levels of posterior noise in our previous research. In this paper, we extend this analysis to several different noise models, and show that the Voting algorithm remains robust in all of them. We consider the prior noise model and the partial evaluation of randomly selected bits. The Voting algorithm has superior runtime bounds on these problems compared to other published algorithms. We also introduce a new variant of partial evaluation, and further consider the simple model when a comparison-based oracle produces incorrect results with a fixed probability. [ABSTRACT FROM AUTHOR]