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Conflict Resolution and Optimization

Subject Area Computer Architecture, Embedded and Massively Parallel Systems
Theoretical Computer Science
Term from 2013 to 2020
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 206480214
 
Final Report Year 2020

Final Report Abstract

The goal of Project B1, “Conflict Resolution and Optimization”, was the development of general methods and tools for changing applications on flexible platforms that can be used in different and changing circumstances. Our objectives were to combine aspects of theory (developing general methods) and practice (aiming at practical methods that are useful in specific application scenarios). A particular focus was on scenarios with the necessity to adapt to incremental changes, which required developing sound theoretical methods, in the general context of the Research Unit as well as in specific application scenarios. Another challenge was to develop the mathematical background and tools for the practical projects of the Research Unit. First, we studied a number of new results for the aspects of Reallocation and Reconfiguration. For scheduling tasks in the context of change, we developed methods for achieving and maintaining feasibility of a solution, possibly at the expense of performing some change to a preliminary schedule. For a variety of applications in space, we worked in close cooperation with research unit C1 for developing energy-efficient computation and computation with limited resources, with a special focus on reconfigurable computing on FPGAs for practical applications in space missions. Second, we provided a number of important optimization methods for Concurrency, Conflicts and Allocation. We were able to provide fundamental progress in the context of concurrent robot navigation, where the objective is to coordinate the collision-free reconfiguration of a (potentially large) swarm of mobile components in an efficient manner, making use of parallelism. We also achieved algorithmic breakthroughs for a variety of problems in conflict-free coloring, including for multi-criteria optimization. For aspects of resource allocation corresponding to geometric packing, we were able to establish tight, new worst-case bounds on achievable packing density. Third, we developed new ideas and gave results for Distributed and Robust Methods, and Game Theory. We introduced and established the power of a new concept for parallel and robust optimization with incomplete information, inspired by concepts of redundancy. For this notion of k-copy online algorithms, we provided scenarios and methods that can beat the established lower bounds for single decision processes, making constructive use of the power of redundancy. Combining competitive and cooperative elements of resource allocation scenarios, we also studied efficient resource allocation from a game-theoretic point of view, and gave results on equilibria and algorithmic strategies.

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