Merge Sort

Merge Sort

Big O(n) => linear time complexity.

Following is sample javascript code.

function mergeSort(arr) {
  if(arr.length <= 1) {
    return arr;
  }
  let middle = Math.floor(arr.length-1/2)
  let left = arr.slice(0, middle);
  let right = arr.slice(middle);
  console.log("r1")
  return merge(mergeSort(left), mergeSort(right))
}
// function mergeSort(arr) {
//   // Base case
//   if (arr.length <= 1) return arr
//   let mid = Math.floor(arr.length / 2)
//   // Recursive calls
//   let left = mergeSort(arr.slice(0, mid))
//   console.log("r1")
//   let right = mergeSort(arr.slice(mid))
//   console.log("r2")
//   return merge(left, right)
// }

console.log(mergeSort([1, 2, 10, 4,6,8,3,5,10,20]))
function merge(left, right) {
  console.log("r3")
  let sortedArr = [] // the sorted items will go here
  while (left.length && right.length) {
    // Insert the smallest item into sortedArr
    if (left[0] < right[0]) {
      sortedArr.push(left.shift())
    } else {
      sortedArr.push(right.shift())
    }
  }
  // Use spread operators to create a new array, combining the three arrays
  return [...sortedArr, ...left, ...right]
}

input:

console.log(mergeSort([1, 2, 10, 4,6,8,3,5,10,20]))

output:
[
  1, 2,  3,  4,  5,
  6, 8, 10, 10, 20
]