We must remember that programs can be defined pretty much as data structures + algorithms. As we conclude this exploration we'll be discussing Searching algorithms and Dynamic Programming.

On CS Algorithms: Chapter II

We must remember that programs can be defined pretty much as data structures + algorithms. As we conclude this exploration we'll be discussing Searching algorithms and Dynamic Programming.

Searching + Traversal, BFS, DFS

We'll talk about:

  • Searching + Traversal
  • Breadth-first search (BFS), Depth-first search (DFS)

Searching is super useful:

  • We search Google, YouTube, search in file.
  • How computers/software can search so fast? Some strategies

    • Linear Search
    • Binary Search
    • BFS
    • DFS

Linear / Sequential Search

  • Find target value within a list.
  • Like loop through arrays to find items.

    • Check one-by-one.
  • Best case O(1) and worst case check every single item O(n)
  • Some JS strategies using linear:
var beasts = ['Centaur', 'Godzilla'];

// ALL are linear


return item === 'Godzilla';

// return item instead of index
return item === 'Godzilla';

// true / false

We can't use this on Google or Facebook, since it will cost time.

Binary Search

Good on sorted lists. E.g. you're looking for 34 and list is sorted: 1, 4, 5, 9, 34, 45

Instead of checking everything you can do binary search:

  • You can start in middle of list:

    • Is 9 higher than 34? NO. Then discard to left.
    • Go to middle of group again and do comparison.
  • If it's sorted we can do better than O(n), creating a binary search tree. Data complexity since its a tree then it gives great storage too.
  • Divide and conquer too.
  • Binary search has lookup method by implementing trees as we saw on tree DS.
  • This has O(log n).
  • But sometimes we need to traverse all nodes, there are other strategies for that.

Traversal - Graph + Tree Traversals

Sometimes we want to add color property to all nodes. Traversals is like visiting every node.

  • How can we traverse a Tree / Graph?

    • With Breadth-first search (BFS), Depth-first search (DFS).
    • Both have O(n).
  • Main benefit we don't put them on Arrays or Hash Tables is order and good O(log n) instead of O(n).
  • Trees / Graphs are used a lot in traversal.

Breadth-first search (BFS) & Depth-first search (DFS)

BFS & DFS Intro

  • BFS:

    • Start with root node and move left to right across second, third level, and so on.
    • Until you find node you're looking OR tree ends.
    • BFS uses more memory b/c it tracks child nodes. So there are implications
  • DFS:

    • Follows one branch until is on last leave then moves forward checking unexplored children and branches.
    • Lower memory requirement. Don't need to store all child pointers.
    • So until it hits dead end then moves.
  • BFS / DFS:
//     9
//  4     20
//1  6  15  170

// BFS: Visits all levels and outputs a list as it finds what it needs 
// [9,4,20,1,6,15,17]

// DFS: List is different
// [9,4,1,6,20, 15,17]


BFS is like water from top to down. DFS is like lines.

When to use BFS or DFS? Time complexity is same for both O(n)

  • BFS:

    • Good for finding shortest path. As it checks closer nodes.
    • Bad: More memory
    • If you have additional info and know where the node is, use BFS.
  • DFS:

    • When you know node is in lower level then use DFS.
    • Ask the question does the path exist
    • Good: Less memory
    • Bad:** it can get slow if tree is very deep not very good finding shortest path**
  • Good Resource.

Common Interview Questions BFS vs DFS

Study to see how you explain to interviewer

  1. If you know a solution is NOT far from the root of the tree:

    • BFS - starts searching closest nodes first.
  2. If the tree is VERY deep and solutions are rare,

    • BFS (DFS will take long time. )
  3. If the tree is very wide:

    • DFS (BFS will need too much memory as it has to keeps track of all the nodes)
  4. If solutions are frequent but located DEEP in the tree

    • DFS
  5. determining whether a path exists between two nodes

    • DFS
  6. Finding the shortest path

    • BFS

Coding BFS & DFS

Breadth-first Search - Exercises

  • Code Here.
  • Uses a queue array
  • Usually is done with iteration.

Breadth-first search (Recursive) - Exercises

Depth-first search - Exercises

  • Code here.
  • stack DS used here as it is Recursion. Height of tree will tell how much memory, memory consumption will depend on that.
  • preOrder: from parent
  • inOrder: order of nodes
  • postOrder: order from trees at the bottom
  • Play with code and console log to see how it inputs
  • main difference is the order you get output
  • depending on your needs you might implement one or the other
  • You just learned how to traverse through tree try to implement in a graph!

Validate a BST - Interview Qs

  • A very common question you will get asked in an interview is how to validate a binary search tree! Hint, you want to use BFS for this.
  • Try it here.

Graph Traversals

  • Tree traversal will be the same as Graph traversal. Trees are a type of graphs.
  • Graphs model real life, like recommendation engine (items related) using like BFS. Facebook what type of friend request or LinkedIn connection with DFS.
  • See it in action


  • DFS is for shortest path. Closest to our node. Related items on Amazon, closest connections.
  • DFS check if it exists. We can go very deep very fast.
  • BFS in Graphs:

    • What's the closest node to 0? BFS is very good as it looks closest nodes first.
  • DFS in Graphs:

    • Great for solving a maze. Because it goes deep then backtracks to solve the puzzle until finds exit.
    • Idea of backtracking after dead end is just recursion.
    • It answers does the path exist?
    • If very deep branch can get very slow. Needs to keep track of all the functions on the stack.

Dijkstra + Bellman-Ford Algorithms

  • In interview you'll probably don't implement them
  • BFS doesn't care about weight. So doesn't take to account weighted graphs.
  • Dijkstra / Bellman-Ford Algorithms:

    • Find the shortest path between two nodes on a weighted graph.
    • Bellman can accommodate negative weights and Dijkstra doesn't.
    • Dijkstra is faster than Bellman's (worst case is O(n^2)).

In an Interview:

  • These algorithms are very complex
  • Know that they exist and how to use them.
  • If you see a weighted graph and asked to find best algorithm:

    • BFS can't be used because is weighted
    • Negative weights? If YES, Bellman. If NO, Dijkstra.

Read more.

Searching + Traversal Review

  • Searching and Traversal are one of the most popular algorithms.
  • Learned about how to traverse tree/graph.
  • Now we're better to equipped to answer questions.
  • Review the Technical Interview Mind Map.
  • More here.

Dynamic Programming (DP)

  • DP is just optimization technique using cache. If you can cache then you can do DP. So its just a buzz word a lot of times.
  • DP solves problems by breaking it down into sub problems, storing solutions in case those sub problems arise.
  • DP

    • Divide and Conquer + Memoization (cache)


  • Caching is a way to store values that you can use later. Like a backpack that holds items you need.
  • Memoization is a specific caching we use in our programs.

Memoization example

function add80(n) {
    return n + 80


What if this operation took a long time? Can we optimize it with memoization?

function add80(n) {
    return n + 80

let c = {}

function memoAdd80(n) {
    if (n in cache) {
        return cache[n]
    } else {
        cache[n] = n + 80
        return cache[n]

What is then memoization?

  • Caches return value based on parameters. If param of function doesn't change then its memoized then it will return cache version. If parameter changes then calculates as usual.
  • So its a way to remember a solution to a problem so you don't have to calculate again.

Closures in Memoization

Patterns in DP:

function memoAdd80() {
    let cache = {}
    //closure returns a function
    return function(n) {
      if (n in cache) {
          return cache[n]
      } else {
          cache[n] = n + 80
          return cache[n]

const memoized = memoAdd80()
console.log(1, memoized(6))
// console.log(cache)
// console.log('-----------')
console.log(2, memoized(6))
  • Closures return a fn inside a fn
  • Because of closure we can avoid global scope and access cache.

Fibonacci and DP (caching)

Remember recursion example.

let calculations = 0
function fib(n) {
    if (n < 1) {
        return n

    return fib(n-1) + fib(n-2)

//will take a long time
// fib(30)
  • Remember this is pretty bad! O(2^n). We'll never want to implement this in real life.

    • This because we repeat a lot of calculations.
  • Can we fix this with caching (DP)? Reduce it to O(n)

    • Yes we can because the solution is optimal and we do the same.
    • We can actually return a memoized version with O(1) and use the cached version.

Solving fibonacci. See below or here.

Note: we increased the space complexity with memoization.

Assess when to use DP (caching / memoization)

Think about your problem like this to implement DP:

  1. Can it be divided into subproblem. Problem broken down into smaller problems.
  2. Recursive solution
  3. Are there repetitive sub-problems?

    • Same calculation over and over? we can memoize the problem!
  4. Memoize sub-problems
  5. Implement caching! and save a lot of calculations!

Interview Questions: Dynamic Programming

Here are some popular Dynamic Programming Questions (see if you can notice the pattern):

  1. House Robber
  2. Best Time to Buy and Sell Stock
  3. Climbing Stairs

Review of DP

  • All you need is to remember the pattern of saving a cache variable and use closures to return a function within a function.
  • If there's repeated calculation then get the memoized result.
  • Memoized version has more space complexity, so this tradeoff is necessary.
  • Other way to incorporate DP is bottom up approach.
  • This one avoids recursion. Starts from simple to higher solutions.
  • This one is harder to know how/when to use
  • Interview is unlikely that they will ask you to implement both methods.
  • In short memoization help us to avoid having to do repeated tasks. If you can implement this you're on your way to become a great engineer.