bigO.js

// Constant runtime - Big O Notation:  "O (1)"
function log(array) {
  console.log(array[0]);
  console.log(array[1]);
 }
  
 log([1, 2, 3, 4]);
 log([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
  
  
 // Linear runtime - Big O Notation:  "O (n)"
 function logAll(array) {
  for (var i = 0; i < array.length; i++) {
    console.log(array[i]); 
  }
 }
  
 logAll([1, 2, 3, 4, 5]);
 logAll([1, 2, 3, 4, 5, 6]);
 logAll([1, 2, 3, 4, 5, 6, 7]);
  
  
 // Exponential runtime - Big O Notation: "O (n^2)"
 function addAndLog(array) {
  for (var i = 0; i < array.length; i++) {
    for (var j = 0; j < array.length; j++) {
      console.log(array[i] + array[j]);
    }
  } 
 }
  
 addAndLog(['A', 'B', 'C']);  // 9 pairs logged out
 addAndLog(['A', 'B', 'C', 'D']);  // 16 pairs logged out
 addAndLog(['A', 'B', 'C', 'D', 'E']);  // 25 pairs logged out
  
  
 // Logarithmic runtime - Big O Notation: O (log n)
 function binarySearch(array, key) {
     var low = 0;
     var high = array.length - 1;
     var mid;
     var element;
     
     while (low <= high) {
         mid = Math.floor((low + high) / 2, 10);
         element = array[mid];
         if (element < key) {
             low = mid + 1;
         } else if (element > key) {
             high = mid - 1;
         } else {
             return mid;
         }
     }
     return -1;
 }