Algorithm
algorithm -- A description of the solution steps for a particular problem
Features: input--0 or more inputs
Output--1 or more outputs
have poor sex
Certainty
Feasibility
How to evaluate an algorithm for good or bad?
The right
High readability
High time efficiency
Small storage space
Time complexity of the algorithm
At the time of the algorithm analysis, the total number of executions of the statement T (N) is a function of the problem size n, which then analyzes the change of T (n) with N and determines the order of magnitude of T (N). The time complexity of the algorithm, which is the time measurement of the algorithm, is recorded as: T (n) = O (f (n)).
It indicates that with the increase of the problem size n, the growth rate of the algorithm execution time is the same as the growth rate of f (n), which is called the asymptotic time complexity of the algorithm, referred to as the time complexity . where f (n) is a function of the problem size n.
Derivation of the large O-order method
1.
Replaces all the addition constants in the run time with constants.
2.
In the modified run Count function, only the highest order is preserved.
3.
If the highest order exists and is not, the constant multiplied by the item is removed. The result is the Big O-order.
Common time complexities such as tables:
Algorithm of data structure