I'm looking to learn more about data structures, time complexity, and efficient algorithms. I have solved the [Left Rotation Problem][1] [1]: https://www.hackerrank.com/challenges/array-left-rotation/problem on HackerRank using JavaScript. I am looking to see ways to optimize the time complexity. I feel like I have a lot to learn, as don't grok Big O.
My solution:
function main() {
const nd = readLine().split(' ')
const numbers = parseInt(nd[0], 10)
const rotations = parseInt(nd[1], 10)
const arr = readLine().split(' ').map(aTemp => parseInt(aTemp, 10))
const front = arr.slice(0, rotations)
const back = arr.slice(rotations, numbers)
const new_arr = back.concat(front)
console.log(new_arr.join(' '))
}
function main() {
const nd = readLine().split(' ')
const numbers = parseInt(nd[0], 10)
const rotations = parseInt(nd[1], 10)
const arr = readLine().split(' ').map(aTemp => parseInt(aTemp, 10))
const front = arr.slice(0, rotations)
const back = arr.slice(rotations, numbers)
const new_arr = back.concat(front)
console.log(new_arr.join(' '))
}
I feel like this likely has bad time complexity, as under the hood, I believe the slice function uses a loop and has a time complexity of O(n), in\$O(n)\$. In addition, I'm unsure of how JavaScript implements the merge from the concat method under the hood.
