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We present the winning solver of the PACE 2019 Implementation Challenge,Vertex Cover Track. The minimum vertex cover problem is one of a handful ofproblems for which kernelization---the repeated reducing of the input size viadata reduction rules---is known to be highly effective in practice. Ouralgorithm uses a portfolio of techniques, including an aggressive kernelizationstrategy, local search, branch-and-reduce, and a state-of-the-artbranch-and-bound solver. Of particular interest is that several of ourtechniques were not from the literature on the vertex over problem: they wereoriginally published to solve the (complementary) maximum independent set andmaximum clique problems.  Aside from illustrating our solver's performance in the PACE 2019Implementation Challenge, our experiments provide several key insights not yetseen before in the literature. First, kernelization can boost the performanceof branch-and-bound clique solvers enough to outperform branch-and-reducesolvers. Second, local search can significantly boost the performance ofbranch-and-reduce solvers. And finally, somewhat surprisingly, kernelizationcan sometimes make branch-and-bound algorithms perform worse than runningbranch-and-bound alone.

WeGotYouCovered: The Winning Solver from the PACE 2019 Implementation Challenge, Vertex Cover Track