01. Algorithm Introduction and List query

As a summary of self-study, this section includes basic algorithm introduction and * * * (later)

Part One.

* Basic concepts of algorithms

A computational process, a method of solving a problem

* Related Review

Recursion: Call yourself and have an end condition

* Two basic properties

Time complexity: One thing to evaluate the efficiency of an algorithm's operation (time spent)

Analogy to some things in life, such as: blink of an eye--an instant, a mental arithmetic question--a few seconds, boil water--a few minutes

In summary, they are different orders of magnitude of time measurement units.

Print (' Hello World ') O (1) Note: In this form, the time complexity is constant, even if there are multiple lines

O (n) Note: N of the loop is n

O (n2) Note: Cycle n*n times

O (n3) Note: Cycle n*n*n times

　　　　　　　Note: The order of magnitude means that in the above process, it is not affected by a row/lines

* A formula used to estimate the run time of the algorithm in time complexity

* Generally, algorithms with high time complexity are slower than algorithms with low complexity

* Common time complexity (sorted by efficiency)

O (1) < O (Logn) < O (n) < O (Nlogn) < O (N2) < O (N2logn) < O (N3)

* How to judge the complexity of time at a glance

The process of halving the cycle, O (LOGN)

Several loops are the complexity of N's several sides.

Spatial complexity: An equation used to estimate the size of the algorithm's memory (space occupied)

"Space Change Time"

01. Algorithm Introduction and List query