BiG(0)

//which code is faster and better measured by 0(n)
//Time complexity
//Depends on number of operation not time, as powerful PC may take less time to run the code
//space complexity
//How much space it should take

//Loop Example(finding array item by looping)
//Best case(omega)
//Average case(theta)
//worst case(0)

//number of operation based on input
// function log(n) {
//   for (let i = 0; i < n; i++) {
//     console.log(i)
//   }
// }

// log(10)

//Drop constant (n+n) = 2n = o(n)
// function log(n) {
//   for (let i = 0; i < n; i++) {
//     console.log(i)
//   }
//   for (let j = 0; j < n; j++) {
//     console.log(j)
//   }
// }

//Time complexity 0(n^2) - 100 items for input number of 10
// function log(n) {
//   for (let i = 0; i < n; i++) {
//     for (let j = 0; j < n; j++) {
//       console.log(i, j)
//     }
//   }
// }

//0n(n * n * n) - 0(n^3) -O(n^2)
// function log(n) {
//   for (let i = 0; i < n; i++) {
//     for (let j = 0; j < n; j++) {

//       for (let k = 0; k < n; k++) {
//         console.log(i, j, k)
//       }
//     }
//   }
// }

//Drop non dominant part O(n^2 + n) = O(n^2)
// function log(n) {
//   for (let i = 0; i < n; i++) {
//     for (let j = 0; j < n; j++) {
//       console.log(i, j)
//     }
//   }

//   for (let k = 0; k < n; k++) {
//     console.log(k)
//   }
// }

// log(10)

//constant time O(1 + 1+ 1) = 0(1)
// function addItems(n) {
//   return n + n + n
// }

// addItems(10)

//O(logN)
//Divide and conquer
//finding the element in shortest possible steps
//0(1)- O(log n)-O(n)-O(nlog n)-o(n^2)
//best possible shorting algorithm for mixed type of data, string(other than number)

//log2^3 = 8
//log2^8 = 3

//o(a) + O(b) = O(a + b)
//o(a) * O(b) = O(a * b)
function log(a, b) {
  for (let i = 0; i < a; i++) {
    console.log(a)
  }
  for (let j = 0; j < b; j++) {
    console.log(b)
  }
}

//Array gotcha
//Adding and removing item from  the last of the array(O(1)) as it doesn't require indexing
//Adding and removing item from beginning or middle of the array(O(n)) as it requires changing indexing
//accessing by index(O(1))

//How  number of operation Increase based on the number of input - how much efficient is the data structure and algorithm is!!

//pointer(understanding) pass by reference

//loop inside loop 0(n^2)
//proportional0(n)
//Divide and conquer 0(log n) -sorting algorithm
//constant  time 0(1)