2019-02-06 Basic Algorithms Lec4

作者: ANPULI | 来源:发表于2019-02-07 22:41 被阅读0次

2019-02-06 Basic Algorithms Lec4
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JavaScript基础

Recursion

function Sums(S, A[1..n])
result = 0
for i from 1 to n
for j from i to n
if A[i]+A[j] = S
result = result + 1
return result

$\frac{n(n+1)}{2}$

talk about	runtime
can	can not
predict how it scales	predict actual runtime

Big-O and $\theta$ notation

Properties:

ignore slow-growing things
reason about relaative growth, e.g., $n^2$ $2n^2$ and $\frac{n^2}{2}$ should be the same

Definition

We shall say that $f(n) = O(g(n))$ if there exist $C, N > 0$ such that $f(n)\leq C\cdot g(n)$ for all $n > N$
For an algorithm with runtime $O(n^2)$ , is it true that $n\rightarrow 2n \Longrightarrow runtime \rightarrow 4runtime$ ?
No, it just says the algorithm is NOT WORSE THAN $O(n^2)$ .

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2019-02-06 Basic Algorithms Lec4

Recursion

Big-O and $\theta$ notation

Properties:

Definition

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2019-02-06 Basic Algorithms Lec4

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2019-02-06 Basic Algorithms Lec4

Recursion

Big-O and notation

Properties:

Definition

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Big-O and $\theta$ notation