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This self-contained introduction to kernelization, a rapidly developing area of preprocessing analysis, is for researchers, professionals, and graduate students in computer science and optimization. It includes recent advances in upper and lower bounds and meta-theorems, and demonstrates methods through extensive examples using a single data set.
Part III presents complexity results and lower bounds, giving negative evidence by way of W[1]-hardness, the Exponential Time Hypothesis, and kernelization lower bounds.All the results and concepts are introduced at a level accessible to graduate students and advanced undergraduate students.
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