ECS 36A Lecture Notes - Lecture 10: Big O Notation, Big Two, Sorting Algorithm

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Ecs 36a - lecture 10 - sorting e(g(n)) = { f(n) c 1 , c 2 , n 0 s. t. , n> n 0 , c , g(n) <= f(n) <= c2g(n) } : f(n) = function of interest, C = some constant, n is to show that we are interested in bounds as the function goes to infinity. The order of magnitude function describes the part of t(n) that increases the fastest as the value of n increases. this is called the big-o notatio n. N log n (n) -- false (have to consider which grows faster, is statement 1 bounded by theta or no?) Summation of p-1 with i starting at 0: a i n i (n (p-1) ) -- true. Log base a of n (log 6 n) -- true. However, it is common that computer scientists instead use an = sign -- for our.

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