The data structure

The way data is organized is called a data structure (int, float, char) lists and dictionaries are also data structures

algorithm

The input
The output
Fineness: The algorithm terminates automatically after a finite number of steps rather than an infinite loop, and each step can be completed in an acceptable amount of time.
Deterministic: Each step in the algorithm has a definite meaning, without ambiguity
Feasibility: Each step of the algorithm is feasible, that is, each step can be performed a limited number of times

If a+b+c=1000 and a^2+b^2=c^2, how can we find the possible combinations of ABC? Solution 1: Enumeration method

For I in range(1,1001): for j in range(1,1001): k = 1000 -i-j if I * I + j*j == k*k: print(I,j,k)Copy the code

The execution efficiency of the machine
Complexity of the algorithm
But in general, the total time of execution varies from machine to machine, but the amount of basic operations performed is roughly the same

Definition:

In computer science, time complexity, also known as time complexity, is a function that qualitatively describes the running time of an algorithm. This is a function that represents the length of the string input to the algorithm. The time complexity is often expressed in large O notation, excluding the lower order and leading coefficients of the function. In this way, the time complexity can be said to be asymptotic, that is, as the size of the input approaches infinity.
Optimal time complexity, worst time complexity, average time complexity
- Consider worst-case complexity: guaranteed performance
- Consider the average time complexity
Time complexity comparison

The O (1) < O (logn) < O (n) < O (nlogn) < O (n ^ 2) < O (n ^ 3) < O (2 ^ n) < O (n!) < O(n^n)

from timeit import Timer

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Speed: 4 > 3 > 1 > 2