# Special Issue \"2021 Selected Papers from Algorithms' Editorial Board Members\"
## 2 Special Issue  
After a careful refereeing process, 12 papers were selected for this issue. As a rule, all submissions have been reviewed by three experts from the corresponding area. Subsequently, the published papers are surveyed in increasing order of their publication dates for this Special Issue. The first accepted paper [1] considers the online variant of the dynamic facility location problem. In the first step of the developed algorithm, an \(\Omega(\log m)\)-competitive fractional solution with \(m\) being the number of facilities is presented. Then, the authors derive a randomized \(O(\log m+\log n)\)-competitive algorithm with \(n\) being the number of clients. In addition, lower bounds on the competitive ratio are proved both for deterministic and randomized algorithms. The second paper [2] deals with the minimum positive influence dominating set problem, which is an APX-hard optimization problem occurring, e.g., in social networks. Due to the complexity status of this problem, fast greedy heuristics are given and compared on a wide range of social and complex networks. In addition, the performance of these heuristics is also compared with the results obtained by the CPLEX solver. The paper [3] deals with the solution of nonlinear equations. In particular, a new family of multi-step iterative schemes is presented which is built from [PERSON]'s scheme, added a Newton step and uses a divided difference operator. The dynamic behavior of this family is analyzed in terms of the parameter values, also with the goal of detecting elements with good stability properties and others with chaotic behavior. Various numerical tests on several functions show the efficiency and stability of the presented family. The paper [4] investigates the network lifetime maximization problem and derives a greedy algorithm for a weighted version of the maximum disjoint dominating sets problem for energy conservation purposes in wireless sensor networks. In addition, an integer linear programming model is given and compared with respect to solution quality and computation time with the greedy heuristic on a large set of 640 instances. It turned out that the suggested approach outperforms recent local search algorithms from the literature. The paper [5] presents a cooperative optimization approach for distributing service points for mobility applications. In contrast to former works, the authors extend previous approaches towards applications in which the satisfaction of demand relies typically on the existence of appropriate pairs or tuples of service stations as it occurs for instance in care sharing systems. The authors apply a new matrix factorization model as surrogate objective function, and then a MILP model is used to generate an optimized solution with respect to the currently known user information. Finally, benchmark scenarios are generated using an idealized simulation of the user interaction, and experimental results are discussed. In the paper [6], a logical analysis of data with the goal of building Boolean formulas represented by so-called patterns is done. After analyzing the computational complexity of the problem under consideration, strings and patterns that are mutually compatible are investigated. Then ILP models are presented for the two problems of Pattern Cover Minimality and Pattern Equivalence. The paper completes with some computational experiments. The paper [7] deals with the problem of scheduling multiprocessor tasks with given precedence constraints and equal processing times in order to minimize the maximum lateness \(L_{max}\). In particular, it is shown that any optimal coloring of a mixed graph is equivalent to the problem of finding an optimal schedule for partially ordered multiprocessor tasks with unit processing times and the minimization of \(L_{max}\). The results presented in this paper can be interpreted as a new scheduling approach for mixed graph coloring problems and vice versa. In the paper [8], the authors apply machine learning (ML) to solve the traveling salesman problem heuristically.