Basic algorithm - time complexity and space complexity

A. Time complexity 

Definitions: In one process algorithm, the required run-time, generally expressed O.

Use the following search algorithm as an example to illustrate the complexity of the time

Question: give you an array (sorted) to find a given element, output index.
Method One: Direct Anzhi inquiry

void serach(int a[],int b,int n)
{ 
   for(int i = 0;i<n;i++)
   {
      if(a[i] == b) cout<<i<<endl;
   }

}

Where n is the length of the array, we can see the time complexity of this approach is to iterate O (n)

Method Two: binary search

void Binary_Serach(int a[],int b,int n)
{
    int mid,left=0,right=n;
    bool flag = false;
    while(left<=right)
    {
        mid = (left+right)/2;
        if(a[mid]==b){
            flag = true;
            cout<<mid<<endl;
        }
        if(a[mid]<b)
        {
            left = mid+1;
        }
        else{
            right = mid-1;
        }
    }
    if(flag == false)
    {
        cout<<"没找到!"<<endl;
    }
    
}

A binary search has been taken for finding half way, i.e. 2 ^X = n-, where X is the time complexity. O = X (log ₂ n-)

The time complexity is used to measure whether a program good way.

II. Space complexity

Definition: complete removal of the outer space of the input and output program requires.

Generally, we declare a variable space complexity is a constant order, declare a one-dimensional array do not know the size of the space complexity is O (n).

We usually use space for time in the process of writing the algorithm.