CS359G Lecture 4: Spectral Partitioning | in theory


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As we will see in a later lecture, there is a nearly linear time algorithm that finds a vector {x} for which the expression {\delta} in the lemma is very close to {1-\lambda_2}, so, overall, for any graph {G} we can find a cut of expansion {O(\sqrt {h(G)})} in nearly linear time.

…for any tunny “run” known to exhibit a stats measurable bias, through just counting (on colossus).

Think of each std deviation worth of amplification as refining the cutset And tuning to the ciphertext, to best decide between improbably candidates (wheel s that in x or still in the edge expansion set)

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