Big O Notation in javascript examples

big-o-es5.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;
}