Table of Contents

Algorithms

Updated: 2024-03-25
18 min read
Algorithms

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Intro

Sort

Insertion sort sorts an array by continuously picking an element, starting from the second element, and inserting it in its correct position in the sorted part of the array to its left. It does this by shifting larger elements one position ahead of their current position, making room for the new element.

def insertion_sort(array):
    for i in range(1, len(array)):
        value = array[i]
        while i > 0 and array[i - 1] > value:
            array[i] = array[i - 1]
            i -= 1
            array[i] = value
    return array

Selection sort works by repeatedly finding the minimum element from the unsorted part of the array and swapping it with the first unsorted element. It continues this process until the whole array is sorted, hence effectively moving the smallest unsorted element to its correct position in each iteration.

def selection_sort(array):
    for i in range(len(array) - 1):
        min_value = i
        for j in range(i + 1, len(array)):
            if array[j] < array[min_value]:
                min_value = j
        temp = array[min_value]
        array[min_value] = array[i]
        array[i] = temp
    return array

Merge sort sorts an array by dividing it into two halves, recursively sorting those halves, and then merging them back together in sorted order.

  1. If the array has more than one element, find the middle of the array.
  2. Divide the array into two halves using the middle index: the left half (left_half) and the right half (right_half).
  3. Recursively sort both halves by calling merge_sort on left_half and right_half.
  4. Merge the sorted halves back into the original array. The merge operation walks through left_half and right_half, and at each step, it copies the smaller element from either left_half or right_half into the original array.
  5. If there are any remaining elements in left_half or right_half after one has been fully copied back into the array, those elements are copied over.
    1. This happens because those remaining elements are guaranteed to be larger than all elements already copied back into the array.

First more simple example of merging already sorted two arrays:

Merge sorted arrays:

def merge(left_ar, right_ar):
    res = []
    left_index, right_index = 0
    while left_index < len(left_ar) and right_index < len(right_ar):
        if left_ar[left_index] < right_ar[right_index]:
            res.append(left_ar[left_index])
            left_index += 1
        else:
            res.append(right_ar[right_index])
            right_index += 1
    res += left_ar[left_index:] + right_ar[right_index:]

def merge_sort(array):
    mid = len(array) / 2
    left_ar = array[:mid]
    right_ar = array[mid:]
    return merge(left_ar, right_ar)

Sort array:

merge-sort

def merge_sort(array):
    if len(array) > 1: # Only sort if array is larger than 1
        mid = len(array) // 2 # middle of the array
 
        # Split the array into two halves
        left_half = array[:mid]
        right_half = array[mid:]
 
        # Recursively sort both halves
        merge_sort(left_half)
        merge_sort(right_half)
 
        left_index = right_index = merged_index = 0
 
        # Merge sorted halves back into the original array
        while left_index < len(left_half) and right_index < len(right_half):
            if left_half[left_index] <= right_half[right_index]:
                array[merged_index] = left_half[left_index]
                left_index += 1
            else:
                array[merged_index] = right_half[right_index]
                right_index += 1
            merged_index += 1
 
        # If any elements left in either half, append them to the array
        while left_index < len(left_half):
            array[merged_index] = left_half[left_index]
            left_index += 1
            merged_index += 1
 
        while right_index < len(right_half):
            array[merged_index] = right_half[right_index]
            right_index += 1
            merged_index += 1

Merge Sort
Merge Sort

def find_target(nums, target):
    left = 0
    right = len(nums) - 1
    
    while left <= right:
        mid = (left + right) // 2
        if nums[mid] == target:
            return mid

        if nums[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    
    return -1

Python build-in module

from bisect import bisect_left

sorted_fruits = ['apple', 'banana', 'orange', 'plum']
bisect_left(sorted_fruits, 'kiwi')

>> 2

Sliding Window

Usage: Use when need to handle the input data in specific window size.

sliding-image

Example: Sliding window technique to find the largest sum of 4 consecutive numbers.

Template:

while j < size:
    # Calculation's happen's here
    # ...

    if condition < k:
        j+=1
    elif condition == k: # res <-- calculation
        j+=1
    elif condition > k:
        while condition > k:
            i+=1 # remove calculation for i
        j+=1
return res

Examples

Problem: Find the largest sum of k consecutive entries, given an array of size n.

  1. Add the first k components together and save the result in the currentSum variable. Because this is the first sum, it is also the current maximum; thus, save it in the variable maximumSum.
  2. As the window size is ww, we move the window one place to the right and compute the sum of the items in the window.
  3. Update the maximum if the currentSum is greater than the maximumSum, and repeat step 2.
def max_sum(arr, k):
    n = len(arr)  # length of the array

    if n < k:  # length of array must be greater window size
        print("Invalid")
        return -1

    # sum of first k elements
    window_sum = sum(arr[:k])
    max_sum = window_sum

    # remove the  first element of previous
    # window and add the last element of
    # the current window to calculate the 
    # the sums of remaining windows
    for i in range(n - k):
        window_sum = window_sum - arr[i] + arr[i + k]
        max_sum = max(window_sum, max_sum)

    return max_sum


arr = [16, 12, 9, 19, 11, 8]
k = 3
print(max_sum(arr, k))

Problem: Find duplicates within a range k in an array

Input: nums = [5, 6, 8, 2, 4, 6, 9]
k = 2
Ouput: False

def get_duplicates(nums, k):
    d = {}
    count = 0
    for i in range(len(nums)):
        if nums[i] in d and i - d[nums[i]] <= k:
            return True
        else:
            d[nums[i]] = i
    
    return False

Two Pointers

A classic way of writing a two-pointer sliding window. The right pointer keeps moving to the right until it cannot move to the right (the specific conditions depend on the topic). When the right pointer reaches the far right, start to move the left pointer to release the left boundary of the window.

Usage: Use two pointers to iterate the input data. Generally, both pointers move in the opposite direction at a constant interval.

two-pointers.jpg

Two-Pass Approach

The two-pass approach is a common algorithmic pattern used to solve problems by going through the data twice. In the first pass, you gather some information that you’ll use in the second pass to solve the problem. Here is an explanation of the two-pass approach with two examples.

First Pass: Gather Information

The first pass is used to collect some information from the data that will be useful to solve the problem. This could involve counting the occurrence of items, finding the maximum or minimum value, or performing some other calculation that will help in the second pass. Second Pass: Solve the Problem

Using the information gathered in the first pass, you can now go through the data again to solve the problem. Let’s go through two examples to understand this approach better.

Example 1: Finding the Relative Rank of Scores

You have a list of scores and you want to find out the relative rank of each score in descending order.

  1. First Pass: Sort the list in descending order.
  2. Second Pass: Create a new list with the rank of each score in the original list.
scores = [95, 85, 90, 100]
sorted_scores = sorted(scores, reverse=True)
ranking = {score: i + 1 for i, score in enumerate(sorted_scores)}

for score in scores:
    print("Score:", score, "Rank:", ranking[score])

Output:

Score: 95 Rank: 2
Score: 85 Rank: 4
Score: 90 Rank: 3
Score: 100 Rank: 1

Example 2: Find if there’s a pair of numbers in an array that add up to a target value

  1. First Pass: Create a diccionario that keeps track of the occurrence of each number in the list.
  2. Second Pass: For each number in the array, check if there is another number in the diccionario that adds up to the target value.
nums = [2, 3, 7, 11, 15]
target = 9
counter = {}

for num in nums:
    counter[num] = counter.get(num, 0) + 1

for num in nums:
    diff = target - num
    if diff in counter:
        if diff != num or counter[num] > 1:
            print("Pair:", (num, diff))
            break

Output:

Pair: (2, 7)

In both examples, the first pass through the data gathered information that was then used in the second pass to solve the problem.

Prefix Sums

Usage: The prefix sums algorithm is a powerful technique that can simplify problems that require frequent computation of sums over subarrays or subtrees.

The idea behind the prefix sums algorithm is to preprocess an array of numbers such that you can efficiently answer queries about the sum of elements in a given subarray.

Imagine you’re given an array of numbers, and you want to know the sum of numbers between the indices i and j (inclusive). If you sum up the numbers every time you receive such a query, your solution will be slow. This is where prefix sums come into play.

Abstract Example

Consider an array arr:

arr = [a, b, c, d, e]

The prefix sum array prefix_arr, for the above array would be:

prefix_arr = [a, a+b, a+b+c, a+b+c+d, a+b+c+d+e]

arr = [1, 3, 4, 8, 6, 1, 4, 2]
prefix_arr = [1, 4, 8, 16, 22, 23, 27, 29]

Now, to find the sum between indices i and j (inclusive) in array arr, you can simply take:

prefix_arr[j] - prefix_arr[i-1]

(Note: If i=0, then it’s just prefix_arr[j])

Example: Prefix Sums on an Array

def prefix_sums(arr):
    n = len(arr)
    prefix_arr = [0] * (n)
    prefix_arr[0] = arr[0]
    for i in range(1, n):
        prefix_arr[i] = prefix_arr[i-1] + arr[i]
    return prefix_arr

# Using the prefix sum array
def sum_from_i_to_j(prefix_arr, i, j):
    if i == 0:
        return prefix_arr[j]
    return prefix_arr[j] - prefix_arr[i-1]

arr = [2, 3, 7, 1, 5]
prefix_arr = prefix_sums(arr)
print(sum_from_i_to_j(prefix_arr, 1, 3))  # Output: 11

Python Example: Prefix Sums on a TreeNode

Firstly, let’s define a binary tree:

class TreeNode:
    def __init__(self, value=0, left=None, right=None):
        self.value = value
        self.left = left
        self.right = right

Now, we’ll modify the tree such that each node will contain the sum of all nodes in its left subtree plus its own value:

def prefix_sum_tree(node):
    if not node:
        return 0
    left_sum = prefix_sum_tree(node.left)
    # Store prefix sum in the node's value
    node.value += left_sum
    prefix_sum_tree(node.right)
    return node.value + (node.right.value if node.right else 0)

# Example
root = TreeNode(5, TreeNode(3, TreeNode(2), TreeNode(4)), TreeNode(8))
prefix_sum_tree(root)

For the above example, the left child of the root (i.e., the node with value 3) will be updated to 8 (2 + 3 + 4), since 2 and 4 are the left children’s values.

Dynamic programming (DP)

Breadth First Search (BFS)

BFS on Tree:

from collections import deque

class TreeNode:
    def __init__(self, val=0, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right

def bfs_tree(root):
    queue = deque([root])

    while queue:
        node = queue.popleft()
        print(node.val, end=' ')

        if node.left:
            queue.append(node.left)
        if node.right:
            queue.append(node.right)

root = TreeNode(1)
root.left = TreeNode(2)
root.right = TreeNode(3)
root.left.left = TreeNode(4)
root.left.right = TreeNode(5)

bfs_tree(root)

BFS on Graph:

from collections import defaultdict, deque

class Graph:
    def __init__(self):
        self.graph = defaultdict(list)

    def add_edge(self, u, v):
        self.graph[u].append(v)

    def bfs(self, start):
        visited = set()
        queue = deque([start])

        while queue:
            node = queue.popleft()
            if node not in visited:
                print(node, end=' ')
                visited.add(node)

                for neighbor in self.graph[node]:
                    if neighbor not in visited:
                        queue.append(neighbor)

g = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)

g.bfs(2)

Depth-first search (DFS)

DFS on Tree:

def dfs(root, target):
    if root is None:
        return None
    if root.val == target:
        return root
    left = dfs(root.left, target)
    if left is not None:
        return left
    return dfs(root.right, target)

DFS on Graph:

def dfs(root, visited):
    for neighbor in get_neighbors(root):
        if neighbor in visited:
            continue
        visited.add(neighbor)
        dfs(neighbor, visited)

DFS on two-dimensional array:

Let’s imagine you have a big maze made of walls and corridors, and you want to find a way from the entrance to the exit. You can put a robot at the entrance, and you want to tell the robot what to do to find the exit.

The first thing you might tell the robot is to always go as far as it can in one direction before turning. This is what depth-first search does.

The robot starts at the entrance and goes as far as it can down the first corridor it finds.

  • If it comes to a dead end, it goes back to the last intersection it passed and tries the next corridor.
  • If it comes to the exit, it stops and says “I found the exit!”.

Example:

# Define the maze as a two-dimensional array
maze = [
  ['.', '.', '#', '#', '#', '#', '#', '#'],
  ['#', '.', '.', '.', '#', '.', '.', '#'],
  ['#', '.', '#', '.', '#', '.', '.', '#'],
  ['#', '.', '.', '.', '.', '#', '.', '#'],
  ['#', '#', '#', '#', '.', '#', '.', '#'],
  ['#', '.', '.', '.', '.', '.', '.', '#'],
  ['#', '.', '#', '#', '#', '#', '.', '.'],
  ['#', '#', '#', '#', '#', '#', '#', '.'],
]

# Define the starting point and the destination
start = (0, 0)
end = (len(maze)-1, len(maze[0])-1)

# Define a function to find the exit using depth-first search
def dfs(current, visited):
  # Mark the current cell as visited
  visited.add(current)

  # Base case: If we've reached the destination, return True
  # or other condition
  if current == end:
    return True

  # Try all possible directions from the current cell
  for delta in [(0, 1), (1, 0), (0, -1), (-1, 0)]:
    next_cell = (current[0] + delta[0], current[1] + delta[1])
    if is_valid_cell(next_cell) and next_cell not in visited:
      if dfs(next_cell, visited):
        return **True**

  # If we couldn't find the exit from this cell, backtrack to the previous cell
  return False

# Call the depth-first search function with the starting point and an empty set of visited cells
visited = set()
if dfs(start, visited):
  print("I found the exit!")
else:
  print("I couldn't find the exit.")

Base template:

def dfs(matrix, row, col, visited):
    # Check if the current cell is out of bounds or has already been visited
    if (
        row < 0 or
        row >= len(matrix) or
        col < 0 or
        col >= len(matrix[0]) or
        visited[row][col]:
    )
        return

    # Mark the current cell as visited
    visited[row][col] = True

    # Define the possible directions to move (right, down, left, up)
    directions = [(0, 1), (1, 0), (0, -1), (-1, 0)]

    # Iterate through the directions and call DFS recursively on neighboring cells
    for step_row, step_col in directions:
        new_row, new_col = row + step_row, step_col + dc
        dfs(matrix, new_row, new_col, visited)

Backtracking

Based on Depth-first search (DFS)

Usage:

Finding all permutations, combinations, subsets and solving sudoku are classic combinatorial problems.

Imagine you are trying to solve a puzzle, like a Sudoku. When you are solving a puzzle, sometimes you reach a point where you can’t make any more progress using the current path. That’s when you need to backtrack.

Backtracking is a general algorithmic technique that is used to find all (or some) solutions to a problem by incrementally building candidates, and checking if the candidate is feasible or not. If the candidate is not feasible, the algorithm goes back (backtracks) to the previous step and tries again with a different candidate. The process continues until a solution is found, or all candidates have been tried.

Backtracking is an algorithmic technique for solving problems recursively by trying to build a solution incrementally, one piece at a time, removing those solutions that fail to satisfy the constraints of the problem at any point of time.

Backtracking algorithm is derived from the Recursion algorithm, with the option to revert if a recursive solution fails, i.e. in case a solution fails, the program traces back to the moment where it failed and builds on another solution. So basically it tries out all the possible solutions and finds the correct one.

Backtracking == DFS on a tree

Howto:

  1. Backtracking is drawing tree
  2. When drawing the tree, bear in mind:
    • how do we know if we have reached a solution?
    • how do we branch (generate possible children)?

Example:

Let’s say we want to generate all possible combinations of 1, 2, and 3 of length 2. The possible combinations are: (1, 2), (1, 3), (2, 1), (2, 3), (3, 1), (3, 2).

This process generates all possible combinations of length k:

def backtrack(nums, path, res, k):
    # nums: the list of available numbers
    # path: the current path of selected numbers
    # res: the list of all valid combinations
    # k: the length of each combination
    
    if len(path) == k: # base case
        res.append(path[:])
        return

    for i in range(len(nums)):
        path.append(nums[i])
        backtrack(nums[:i] + nums[i+1:], path, res, k)
        path.pop()


nums = [1, 2, 3]
k = 2
res = []
backtrack(nums, [], res, k)
print(res)
# [[1, 2], [1, 3], [2, 1], [2, 3], [3, 1], [3, 2]]

Algorithm:

  1. We start with an empty path and empty result list.
  2. We loop through the available numbers (1, 2, 3) and add the first number to the path.
  3. We make a recursive call to backtrack with the remaining numbers (2, 3) and a path that includes the first number (e.g., [1]). This adds all possible combinations of length k-1 with the first number.
  4. After the recursive call, we remove the first number from the path.
  5. We repeat this process for the other available numbers, generating all possible combinations of length k.
  6. When we reach the base case (len(path) == k), we add the current path to the result list.
  7. We return the result list of all possible combinations.

The base case is when the length of the path is equal to k. At this point, we add the current path to the result list and return.

The recursive case involves looping through the available numbers, adding the current number to the path, making a recursive call with the remaining numbers, and removing the current number from the path after the recursive call.

path:

In the backtrack function, path refers to the list of numbers that have been selected so far to form a valid combination.

Initially, path is an empty list []. In each recursive call, a number from nums is selected and added to path.

For example, if nums = [1, 2, 3] and the current path is [1], the function will call backtrack([2, 3], [1], res, k) to consider all possible combinations with 1 in the first position, followed by all possible combinations of length k-1 of [2, 3] in the second position.

Once all possible combinations with 1 in the first position have been explored, the number 1 will be removed from path, and the function will try the next number from nums, which in this case is 2. The function continues in this way until all valid combinations of length k have been found and added to the res list.

Problem examples:

Example of LeetCode 78 problem:

Algorithm:

We define a backtrack function named backtrack(first, curr) which takes the index of first element to add and a current combination as arguments.

  1. If the current combination is done, we add the combination to the final output.

  2. Otherwise, we iterate over the indexes i from first to the length of the entire sequence n.

    1. Add integer nums[i] into the current combination curr.
    2. Proceed to add more integers into the combination: backtrack(i + 1, curr).
    3. Backtrack by removing nums[i] from curr.
class Solution:
    def subsets(self, nums: List[int]) -> List[List[int]]:
        def backtrack(first = 0, curr = []):
            # if the combination is done
            if len(curr) == k:  
                output.append(curr[:])
                return
            for i in range(first, n):
                # add nums[i] into the current combination
                curr.append(nums[i])
                # use next integers to complete the combination
                backtrack(i + 1, curr)
                # backtrack
                curr.pop()
        
        output = []
        n = len(nums)
        for k in range(n + 1):
            backtrack()
        return output


# [    [], 
#     [1], [3], [4], 
#     [1, 3], [1, 4], [3, 4], 
#     [1, 3, 4]
# ]

Trie

class TrieNode:
   def __init__(self):
       self.children = {}
       self.is_end = False

class Trie:
   def insert(self, word: str) -> None:
       current_node = self.root
       for char in word:
           if char not in current_node.children:
               new_node = TrieNode()
               current_node.children[char] = new_node
               current_node = new_node
           else:
               current_node = current_node.children[char]
       current_node.is_end = True

   def search(self, word: str) -> bool:
       current_node = self.root
       for char in word:
           if char not in current_node.children:
               return False
           current_node = current_node.children[char]
       return current_node.is_end

   def startsWith(self, prefix: str) -> bool:
       current_node = self.root
       for char in prefix:
           if char not in current_node.children:
               return False
           current_node = current_node.children[char]
       return True

Dutch National Flag problem

The Dutch National Flag problem is a sorting problem that asks us to sort an array of colors, like a bunch of different colored socks. We want to put all the socks of the same color together in the array.

The colors in this problem are represented by numbers. We use the numbers 0, 1, and 2 to represent the colors red, white, and blue. So, we have an array of numbers, and we want to sort them in such a way that all the 0's are at the beginning of the array, then all the 1's, and finally all the 2's are at the end.

For example, if we have an array [2, 0, 2, 1, 1, 0], we want to sort it so that it becomes [0, 0, 1, 1, 2, 2].

One way to solve this problem is to use a technique called the Dutch National Flag algorithm. The idea behind this algorithm is to use three pointers: a low pointer, a mid pointer, and a high pointer.

The low pointer starts at the beginning of the array, the high pointer starts at the end of the array, and the mid pointer starts at the beginning of the array.

We then iterate through the array with the mid pointer.

  • If the value at the mid pointer is 0, we swap it with the value at the low pointer and increment both pointers. - If the value at the mid pointer is 1, we leave it where it is and just increment the mid pointer.
  • If the value at the mid pointer is 2, we swap it with the value at the high pointer and decrement the high pointer.

We keep doing this until the mid pointer passes the high pointer, at which point the array is sorted.

So, in our sock example, we start with the low pointer at the beginning of the array, the mid pointer also at the beginning of the array, and the high pointer at the end of the array. Then, we iterate through the array with the mid pointer, swapping socks as needed until the array is sorted by color.

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