Big O notation(recap) part 1

In Programming, we hear thousands of times about time complexity and space complexity. Why it is essential to understand and to study? Because in the real-world, somewhere has to store all the computers(servers, database, all hardware). So when we talk about time and space complexity we talk through what it calls Big O. Now Big o notation is not a method or sort of function. It’s simply nothing but an algorithm that descript the performance such as time and space used. So let’s talk about it.

Resources: https://biercoff.com/big-o-complexity-cool-cheat-sheet/

Big Os

1. O(1) Constant

no loop, O(1) or constant represent that time(or space) complexity is at constant.

function bigOconstant(){
 var greeting = "hello"
     console.log(greeting")
} 

2. O(log N) Logarithmic

Usually, searching algorithms have log N if there are sorted such as binary search.

function bigOLogarithmic(arr, target){
  var start = 0;
  var end = arr.length -1;
  while(start <= end){
    var middle = (start + end) /2;
    var current = arr[middle];
    if(current > target) {
       start = middle - 1;
     } else if(current < target){
       low = middle + 1; 
     } else {
      return middle;
 }
}

3. O(n) Linear

Example like for loops, while loops through n items.

function bigOlinear(arr, target){
 for(var i=0; i< arr.length; i++){
     if(arr[i] === target){
     retrun arr[i]
     }
   }
  return console.log("not found!")
}