CS369N: Beyond Worst-Case Analysis Lecture #5: Self-Improving Algorithms
نویسنده
چکیده
Last lecture concluded with a discussion of semi-random graph models, an interpolation between worst-case analysis and average-case analysis designed to identify robust algorithms in the face of strong impossibility results for worst-case guarantees. This lecture and the next two give three more analysis frameworks that blend aspects of worstand average-case analysis. Today’s model, of self-improving algorithms, is the closest to traditional averagecase analysis. The model and results are by Ailon, Chazelle, Comandar, and Liu [1].
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