@inproceedings{17653, author = {{Polevoy, Gleb and de Weerdt, M.M.}}, booktitle = {{Proceedings of the 29th Benelux Conference on Artificial Intelligence}}, keywords = {{interaction, reciprocation, contribute, shared effort, curbing, convergence, threshold, Nash equilibrium, social welfare, efficiency, price of anarchy, price of stability}}, publisher = {{Springer}}, title = {{{Reciprocation Effort Games}}}, year = {{2017}}, } @inproceedings{17654, author = {{Polevoy, Gleb and de Weerdt, M.M.}}, booktitle = {{Proceedings of the 29th Benelux Conference on Artificial Intelligence}}, keywords = {{agents, projects, contribute, shared effort game, competition, quota, threshold, Nash equilibrium, social welfare, efficiency, price of anarchy, price of stability}}, publisher = {{Springer}}, title = {{{Competition between Cooperative Projects}}}, year = {{2017}}, } @unpublished{17811, abstract = {{We consider a swarm of $n$ autonomous mobile robots, distributed on a 2-dimensional grid. A basic task for such a swarm is the gathering process: All robots have to gather at one (not predefined) place. A common local model for extremely simple robots is the following: The robots do not have a common compass, only have a constant viewing radius, are autonomous and indistinguishable, can move at most a constant distance in each step, cannot communicate, are oblivious and do not have flags or states. The only gathering algorithm under this robot model, with known runtime bounds, needs $\mathcal{O}(n^2)$ rounds and works in the Euclidean plane. The underlying time model for the algorithm is the fully synchronous $\mathcal{FSYNC}$ model. On the other side, in the case of the 2-dimensional grid, the only known gathering algorithms for the same time and a similar local model additionally require a constant memory, states and "flags" to communicate these states to neighbors in viewing range. They gather in time $\mathcal{O}(n)$. In this paper we contribute the (to the best of our knowledge) first gathering algorithm on the grid that works under the same simple local model as the above mentioned Euclidean plane strategy, i.e., without memory (oblivious), "flags" and states. We prove its correctness and an $\mathcal{O}(n^2)$ time bound in the fully synchronous $\mathcal{FSYNC}$ time model. This time bound matches the time bound of the best known algorithm for the Euclidean plane mentioned above. We say gathering is done if all robots are located within a $2\times 2$ square, because in $\mathcal{FSYNC}$ such configurations cannot be solved.}}, author = {{Fischer, Matthias and Jung, Daniel and Meyer auf der Heide, Friedhelm}}, booktitle = {{arXiv:1702.03400}}, title = {{{Gathering Anonymous, Oblivious Robots on a Grid}}}, year = {{2017}}, } @inproceedings{79, abstract = {{Consider a problem in which $n$ jobs that are classified into $k$ types arrive over time at their release times and are to be scheduled on a single machine so as to minimize the maximum flow time.The machine requires a setup taking $s$ time units whenever it switches from processing jobs of one type to jobs of a different type.We consider the problem as an online problem where each job is only known to the scheduler as soon as it arrives and where the processing time of a job only becomes known upon its completion (non-clairvoyance).We are interested in the potential of simple ``greedy-like'' algorithms.We analyze a modification of the FIFO strategy and show its competitiveness to be $\Theta(\sqrt{n})$, which is optimal for the considered class of algorithms.For $k=2$ types it achieves a constant competitiveness.Our main insight is obtained by an analysis of the smoothed competitiveness.If processing times $p_j$ are independently perturbed to $\hat p_j = (1+X_j)p_j$, we obtain a competitiveness of $O(\sigma^{-2} \log^2 n)$ when $X_j$ is drawn from a uniform or a (truncated) normal distribution with standard deviation $\sigma$.The result proves that bad instances are fragile and ``practically'' one might expect a much better performance than given by the $\Omega(\sqrt{n})$-bound.}}, author = {{Mäcker, Alexander and Malatyali, Manuel and Meyer auf der Heide, Friedhelm and Riechers, Sören}}, booktitle = {{Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)}}, pages = {{207--222}}, publisher = {{Springer}}, title = {{{Non-Clairvoyant Scheduling to Minimize Max Flow Time on a Machine with Setup Times}}}, doi = {{10.1007/978-3-319-89441-6}}, volume = {{10787}}, year = {{2017}}, } @inproceedings{82, abstract = {{Many graph problems such as maximum cut, chromatic number, hamiltonian cycle, and edge dominating set are known to be fixed-parameter tractable (FPT) when parameterized by the treewidth of the input graphs, but become W-hard with respect to the clique-width parameter. Recently, Gajarský et al. proposed a new parameter called modular-width using the notion of modular decomposition of graphs. They showed that the chromatic number problem and the partitioning into paths problem, and hence hamiltonian path and hamiltonian cycle, are FPT when parameterized by this parameter. In this paper, we study modular-width in parameterized parallel complexity and show that the weighted maximum clique problem and the maximum matching problem are fixed-parameter parallel-tractable (FPPT) when parameterized by this parameter.}}, author = {{Abu-Khzam, Faisal N. and Li, Shouwei and Markarian, Christine and Meyer auf der Heide, Friedhelm and Podlipyan, Pavel}}, booktitle = {{Proceedings of the 11th International Workshop on Frontiers in Algorithmics (FAW)}}, pages = {{139--150}}, title = {{{Modular-Width: An Auxiliary Parameter for Parameterized Parallel Complexity}}}, doi = {{10.1007/978-3-319-59605-1_13}}, year = {{2017}}, } @inproceedings{59, abstract = {{We consider a scheduling problem on $m$ identical processors sharing an arbitrarily divisible resource. In addition to assigning jobs to processors, the scheduler must distribute the resource among the processors (e.g., for three processors in shares of 20\%, 15\%, and 65\%) and adjust this distribution over time. Each job $j$ comes with a size $p_j \in \mathbb{R}$ and a resource requirement $r_j > 0$. Jobs do not benefit when receiving a share larger than $r_j$ of the resource. But providing them with a fraction of the resource requirement causes a linear decrease in the processing efficiency. We seek a (non-preemptive) job and resource assignment minimizing the makespan.Our main result is an efficient approximation algorithm which achieves an approximation ratio of $2 + 1/(m-2)$. It can be improved to an (asymptotic) ratio of $1 + 1/(m-1)$ if all jobs have unit size. Our algorithms also imply new results for a well-known bin packing problem with splittable items and a restricted number of allowed item parts per bin.Based upon the above solution, we also derive an approximation algorithm with similar guarantees for a setting in which we introduce so-called tasks each containing several jobs and where we are interested in the average completion time of tasks (a task is completed when all its jobs are completed).}}, author = {{Kling, Peter and Mäcker, Alexander and Riechers, Sören and Skopalik, Alexander}}, booktitle = {{Proceedings of the 29th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA)}}, pages = {{123----132}}, title = {{{Sharing is Caring: Multiprocessor Scheduling with a Sharable Resource}}}, doi = {{10.1145/3087556.3087578}}, year = {{2017}}, } @inproceedings{70, author = {{Feldkord, Björn and Markarian, Christine and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 11th Annual International Conference on Combinatorial Optimization and Applications (COCOA)}}, pages = {{17 -- 31}}, title = {{{Price Fluctuations in Online Leasing}}}, doi = {{10.1007/978-3-319-71147-8_2}}, year = {{2017}}, } @phdthesis{703, author = {{Podlipyan, Pavel}}, publisher = {{Universität Paderborn}}, title = {{{Local Algorithms for the Continuous Gathering Problem}}}, doi = {{10.17619/UNIPB/1-230}}, year = {{2017}}, } @phdthesis{704, author = {{Riechers, Sören}}, publisher = {{Universität Paderborn}}, title = {{{Scheduling with Scarce Resources}}}, doi = {{10.17619/UNIPB/1-231}}, year = {{2017}}, } @article{706, author = {{Mäcker, Alexander and Malatyali, Manuel and Meyer auf der Heide, Friedhelm and Riechers, Sören}}, journal = {{Journal of Combinatorial Optimization}}, number = {{4}}, pages = {{1168--1194}}, publisher = {{Springer}}, title = {{{Cost-efficient Scheduling on Machines from the Cloud}}}, doi = {{10.1007/s10878-017-0198-x}}, volume = {{36}}, year = {{2017}}, }