On equivalent reformulations for absolute value equations

Absolute value equations, Linear complementarity problem,

Abstract

For a simple undirected graph and a given positive integer k, a k-club is a subset of vertices that induces a subgraph of diameter at most k, and the k-club number ¯ωk(G) is the cardinality of a largest k-club in G. In this paper we first prove that for given positive integers k and l, k � = l, the problem of recognizing whether there is a gap between ¯ωk and ¯ωl is NP-hard. Then we use this result to show that for k ≥ 2, unless P = NP, one cannot design a polynomial-time algorithm that would detect a k-club of size> ∆(G) + 1 in any graph G with ¯ωk(G)> ∆(G) + 1, where ∆(G) denotes the maximum degree of a vertex in G. The same results hold for the maximum k-clique problem as well

Laboratory testing of LoRa modulation for CubeSat radio communications

We report the results of the feasibility study of using LoRa modulation for radio communications between CubeSat at low Earth orbit and ground station. The main goal of the study is to define how Doppler effect affects a LoRa radio link. Results of laboratory testing have shown high immunity of LoRa radio link to Doppler shift and a possibility to use LoRa modulation in CubeSat radio communications without any limitations

Laboratory testing of LoRa modulation for CubeSat radio communications

We report the results of the feasibility study of using LoRa modulation for radio communications between CubeSat at low Earth orbit and ground station. The main goal of the study is to define how Doppler effect affects a LoRa radio link. Results of laboratory testing have shown high immunity of LoRa radio link to Doppler shift and a possibility to use LoRa modulation in CubeSat radio communications without any limitations

Two‐stage stochastic minimum s − t cut problems: Formulations, complexity and decomposition algorithms

We introduce the two‐stage stochastic minimum s − t cut problem. Based on a classical linear 0‐1 programming model for the deterministic minimum s − t cut problem, we provide a mathematical programming formulation for the proposed stochastic extension. We show that its constraint matrix loses the total unimodularity property, however, preserves it if the considered graph is a tree. This fact turns out to be not surprising as we prove that the considered problem is NP‐hard in general, but admits a linear time solution algorithm when the graph is a tree. We exploit the special structure of the problem and propose a tailored Benders decomposition algorithm. We evaluate the computational efficiency of this algorithm by solving the Benders dual subproblems as max‐flow problems. For many tested instances, we outperform a standard Benders decomposition by two orders of magnitude with the Benders decomposition exploiting the max‐flow structure of the subproblems

A Token-Based Approach to Sharing Beliefs in a Large Multiagent Team

Abstract. The performance of a cooperative team depends on the views that individual team members build of the environment in which they are operating. Teams with many vehicles and sensors generate a large amount of information from which to create those views. However, bandwidth limitations typically prevent exhaustive sharing of this information. As team size and information diversity grows, it becomes even harder to provide agents with needed information within bandwidth constraints, and it is impractical for members to maintain any detailed information for every team mate. Building on previous token-based algorithms, this chapter presents an approach for efficiently sharing information in large teams. The key distinction from previous work is that this approach models differences in how agents in the team value knowledge and certainty about features. By allowing the tokens passed through the network to passively estimate the value of certain types of information to regions of the network, it is possible to improve token routing through the use of local decision-theoretic models. We show that intelligent routing and stopping can increase the amount of locally useful information received by team members while making more efficient use of agents’ communication resources.

7th International Conference in Network Analysis

Contributions in this volume focus on computationally efficient algorithms and rigorous mathematical theories for analyzing large-scale networks. Researchers and students in mathematics, economics, statistics, computer science and engineering will find this collection a valuable resource filled with the latest research in network analysis. Computational aspects and applications of large-scale networks in market models, neural networks, social networks, power transmission grids, maximum clique problem, telecommunication networks, and complexity graphs are included with new tools for efficient network analysis of large-scale networks. This proceeding is a result of the 7th International Conference in Network Analysis, held at the Higher School of Economics, Nizhny Novgorod in June 2017. The conference brought together scientists, engineers, and researchers from academia, industry, and government