Data structures and algorithms-time complexity and spatial complexity

" definition of algorithmic time complexity"

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.

That is, the number of executions = time

" How to analyze the time complexity of an algorithm?" That is: How to push to the Big O-order? "

-Replaces all addition constants in the run time with constant 1

-Only the highest order is kept in the modified run Count function

-If the highest order exists and is not 1, then the constant multiplied by this is removed

-The final result is the Big O-order

"Common time Complexity"

"The time-consuming complexity of the time is taken from small to large."

"Worst case and average situation"

"The spatial complexity of the algorithm"

Data structures and algorithms-complexity of time and space complexity