time complexity of merge sort
O(n*Log n):
The time complexity of MergeSort is O(n*Log n) in all the 3 cases
(worst, average and best).
As the mergesort always divides the array into two halves
and takes linear time to merge two halves.time complexity of merge sort
O(n*Log n):
The time complexity of MergeSort is O(n*Log n) in all the 3 cases
(worst, average and best).
As the mergesort always divides the array into two halves
and takes linear time to merge two halves.merge sort
// @see https://www.youtube.com/watch?v=es2T6KY45cA&vl=en
// @see https://www.cs.usfca.edu/~galles/visualization/ComparisonSort.html
function merge(list, start, midpoint, end) {
const left = list.slice(start, midpoint);
const right = list.slice(midpoint, end);
for (let topLeft = 0, topRight = 0, i = start; i < end; i += 1) {
if (topLeft >= left.length) {
list[i] = right[topRight++];
} else if (topRight >= right.length) {
list[i] = left[topLeft++];
} else if (left[topLeft] < right[topRight]) {
list[i] = left[topLeft++];
} else {
list[i] = right[topRight++];
}
}
}
function mergesort(list, start = 0, end = undefined) {
if (end === undefined) {
end = list.length;
}
if (end - start > 1) {
const midpoint = ((end + start) / 2) >> 0;
mergesort(list, start, midpoint);
mergesort(list, midpoint, end);
merge(list, start, midpoint, end);
}
return list;
}
mergesort([4, 7, 2, 6, 4, 1, 8, 3]);Copyright © 2021 Codeinu
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