New Frontiers in Parameterized Complexity and Algorithms
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The research on parameterized complexity (PC), described in these peer-reviewed chapters by esteemed researchers, provides ties to practice as well as new theoretical developments, many in the direction of AI. Applications in bioinformatics and social choice are described, as well as faster, less costly approximation methods and new techniques using machine learning to find treewidth and other metrics. Many practical problems can be solved by harnessing the power of both PC and SAT solvers or integer linear programming modeled by parameterized graph problems. The extremely rapid development an...
The research on parameterized complexity (PC), described in these peer-reviewed chapters by esteemed researchers, provides ties to practice as well as new theoretical developments, many in the direction of AI. Applications in bioinformatics and social choice are described, as well as faster, less costly approximation methods and new techniques using machine learning to find treewidth and other metrics. Many practical problems can be solved by harnessing the power of both PC and SAT solvers or integer linear programming modeled by parameterized graph problems. The extremely rapid development and deployment of PC continues to bring fresh understanding and solutions to fundamental open questions.
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