modify errors in latex expressions

This commit is contained in:
Shine wOng
2019-05-07 20:26:40 +08:00
parent 548abe03db
commit 0dddd5b1a4

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@@ -149,16 +149,16 @@ _若存在函数f(n)和正的常数c1和c2使得在渐进的条件(n >> 2)下
所以要进行计算循环的时间复杂度,首先需要知道各种常见级数的求和方法,现列举如下:
+ 幂方级数: 比幂次高出一阶
- $T_2(n) = 1^2 + 2^2 + 3^2 + ... + n^2 = /frac{n(n+1)(2n+1)}{6} = O(n^3)$
- $T_3(n) = 1^3 + 2^3 + 3^3 + ... + n^3 = /frac{n^{2}(n+1)^{2}}{4} = O(n^4)$
- $T_4(n) = 1^4 + 2^4 + 3^4 + ... + n^4 = /frac{n(n+1)(2n+1)(3n^{2}+3n-1)}{30} = O(n^3)$
- $T_2(n) = 1^2 + 2^2 + 3^2 + ... + n^2 = \frac{n(n+1)(2n+1)}{6} = O(n^3)$
- $T_3(n) = 1^3 + 2^3 + 3^3 + ... + n^3 = \frac{n^{2}(n+1)^{2}}{4} = O(n^4)$
- $T_4(n) = 1^4 + 2^4 + 3^4 + ... + n^4 = \frac{n(n+1)(2n+1)(3n^{2}+3n-1)}{30} = O(n^3)$
- ...
- $T_k(n) = 1^k + 2^k + 3^k + ... + n^k = /int_{0}^{n}x^k{d}x = \frac{x^{k+1}}{k+1}|_{0}^{n} = O(n^{k+1}))$
- $T_k(n) = 1^k + 2^k + 3^k + ... + n^k = \int_{0}^{n}x^k{d}x = \frac{x^{k+1}}{k+1}|_{0}^{n} = O(n^{k+1}))$
+ 几何级数: 与末项同阶
+ 收敛级数
+ 不收敛的级数
- 调和级数
- 对数级数 $\log1 + \log2 + \log3 + ... + \logn = \log(n!) = \Theta(n\logn)$
- 对数级数 $log1 + log2 + log3 + ... + logn = log(n!) = \Theta(nlogn)$
邓公说,计算的时候还是要灵活对待。最好可以画一个图分析,图上的每一个点代表一次循环,这样图的面积就是时间复杂度。