adslaton

const connections = new Map();

// create objects in the array
const uf = (n) => {
  for (const x of Array(n).keys()) {
    connections.set({ id: x, name: x}, x);
  } 
}

// root

adslaton

const connections = [];

// create objects in the array
const uf = (n) => {
  for (const x of Array(n).keys()) {
    connections.push({ id: x, name: x});
  } 
}

// root

adslaton

const connections = [                                 // O(1)
  {
    id: 1,
    name: 1
  },
  {
    id: 2,
    name: 2
  },
  {

adslaton

const connections = [
  {
    id: 0,
    name: 0
  },
  {
    id: 1,
    name: 1
  },
  {

adslaton

const connections = [
  {
    id: 0,
    name: 0
  },
  {
    id: 1,
    name: 1
  },
  {

adslaton

const connections = [];

// create objects in the array
const uf = (n) => {
  for (const x of Array(n).keys()) {
    connections.push({ id: x, name: x});
  } 
}

// union

adslaton

const connections = [
  {
    id: 1,
    name: 1
  },
  {
    id: 2,
    name: 2
  },
  {

adslaton

// Represents an algorithm that runs in proportionally to the logarithm of the input size

const f = (n) => {
  for (let i = 1; i < n.length; i = i * 2) {    // 
    console.log(array[n]);                      // O(log(n))
  }                                             //
}

adslaton

// Represents an algorithm whose performance is directly proportional to the squared size of the input data set

for (const x of Array(n).keys()) {      // O(n)
  for (const y of Array(n).keys()) {    //
    console.log(x, y);                  // O(n)
  }                                     //
}

// O(n) * O(n) * O(1) = O(n * n) = O(n^2)

adslaton

// Represents an algorithm who’s complexity will grow in direct proportion to the size of the input data

// 1.

for (const x of Array(n).keys()) {    // n
  console.log(x);                     // O(1)
}

// n * O(1) = O(n)