14 Coding Patterns To Master

This is a collection of coding patterns I have learned to solve not only some of the most common problems, but the 14 patterns (yes, there are way more than 14, but the point here is taking 6 months of preparation and condensing it into a 30 minute read that would not take more than 1-2 weeks to master. I have found these problems and patterns to be the most useful in that the data structures and algorithms are used in many other problems and become familiar over time. Good luck!

Please feel free to comment if you got some value or find any errors!

Thanks!

1. Sliding Window
2. Two Pointers or Iterators
3. Fast and Slow Pointers
4. Merge Intervals
5. Cyclic Sort
6. In-place Reversal of a Linked List
7. Tree Breadth First Search
8. Tree Depth First Search
9. Two Heaps
10. Subsets
11. Modified Binary Search
12. Top K Elements
13. K-way Merge
14. Topological Sort
Conclusion

1. Sliding Window

Problem: Maximum Sum Subarray of Size K

Given an array of integers nums and an integer k, find the maximum sum of any contiguous subarray of size k.

Example: Input: nums = [2, 1, 5, 1, 3, 2], k = 3 Output: 9 (The subarray [5, 1, 3] has the maximum sum of 9)

#Pseudocode:

Initialize maxSum to a negative value to handle negative elements.
Initialize windowSum to 0 to keep track of the sum of the current sliding window.
Iterate through the array from index 0 to length - 1:
- Add the current element to windowSum.
- If the window size is equal to k, update maxSum to be the maximum of maxSum and windowSum.
- If the window size is greater than k, subtract the element from the left of the window and update windowSum.
Return maxSum.

#JavaScript Solution:

function maxSumSubarray(nums, k) {
  let maxSum = -Infinity;
  let windowSum = 0;

  for (let i = 0; i < nums.length; i++) {
    windowSum += nums[i];

    if (i >= k - 1) {
      maxSum = Math.max(maxSum, windowSum);
      windowSum -= nums[i - k + 1];
    }
  }

  return maxSum;
}

#Python Solution:

def max_sum_subarray(nums, k):
    max_sum = float('-inf')
    window_sum = 0

    for i in range(len(nums)):
        window_sum += nums[i]

        if i >= k - 1:
            max_sum = max(max_sum, window_sum)
            window_sum -= nums[i - k + 1]

    return max_sum

Time Complexity - O(N), where N is the number of elements in the nums array. We iterate through the array only once. Space Complexity: O(1), as we use a constant amount of extra space.

2. Longest Substring Without Repeating Characters:

Problem: Longest Substring Without Repeating Characters

Given a string s, find the length of the longest substring without repeating characters.

Example: Input: s = "abcabcbb" Output: 3 (The longest substring without repeating characters is "abc")

#Pseudocode:

Initialize maxLength to 0 to keep track of the maximum length of the substring.
Initialize start to 0 to mark the start index of the current substring.
Create an empty seen dictionary to store the most recent index of each character.
Iterate through the string using the variable end:
- If the current character is in seen and its index is greater than or equal to start:
  - Update start to be the next index of the current character.
- Update the maxLength to be the maximum of the current maxLength and end - start + 1.
- Store the current index of the character in the seen dictionary.
Return maxLength.

#JavaScript Solution:

function longestSubstring(s) {
  let maxLength = 0;
  let start = 0;
  let seen = {};

  for (let end = 0; end < s.length; end++) {
    if (s[end] in seen && seen[s[end]] >= start) {
      start = seen[s[end]] + 1;
    }

    maxLength = Math.max(maxLength, end - start + 1);
    seen[s[end]] = end;
  }

  return maxLength;
}

#Python Solution:

def longest_substring(s):
    max_length = 0
    start = 0
    seen = {}

    for end in range(len(s)):
        if s[end] in seen and seen[s[end]] >= start:
            start = seen[s[end]] + 1

        max_length = max(max_length, end - start + 1)
        seen[s[end]] = end

    return max_length

Time Complexity - O(N), where N is the length of the string s. We iterate through the string only once. Space Complexity: O(min(N, M)), where M is the size of the character set (e.g., ASCII or Unicode). In the worst case, the seen dictionary can store all characters in the character set.

2. Two Pointers or Iterators:

Problem 1: Two Sum

Given an array of integers nums and an integer target, return indices of the two numbers such that they add up to target.

You may assume that each input would have exactly one solution, and you may not use the same element twice.

You can return the answer in any order.

Pseudocode:

Initialize a dictionary, numToIndex, to store the index of each number encountered in the array.
Iterate over the array elements, num and their indices, i:
- Calculate the complement (target - num).
- If the complement is already in numToIndex, return [numToIndex[complement], i].
- Otherwise, store the current num's index in numToIndex.
If no pair is found, return None.

Questions to Ask:

Can the input array have duplicate elements?
Are there guaranteed to be exactly one solution for each input?
Can the input array be empty?

JavaScript Solution:

function twoSum(nums, target) {
  let numToIndex = {};
  for (let i = 0; i < nums.length; i++) {
    let complement = target - nums[i];
    if (complement in numToIndex) {
      return [numToIndex[complement], i];
    }
    numToIndex[nums[i]] = i;
  }
  return null;
}

Time Complexity - O(N), where N is the number of elements in the nums array. Space Complexity - O(N), where N is the number of elements in the nums array.

Python Solution:

def two_sum(nums, target):
    numToIndex = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in numToIndex:
            return [numToIndex[complement], i]
        numToIndex[num] = i
    return None

Time Complexity - O(N), where N is the number of elements in the nums array. Space Complexity - O(N), where N is the number of elements in the nums array.

Problem 2: Container With Most Water

You are given an integer array height of length n. There are n vertical lines drawn such that the two endpoints of the ith line are (i, 0) and (i, height[i]).

Find two lines that together with the x-axis form a container, such that the container contains the most water.

Return the maximum amount of water a container can store.

Notice that you may not slant the container.

Pseudocode:

Initialize two pointers, left at the beginning and right at the end of the array.
Initialize maxArea to store the maximum area found so far.
Iterate until left is less than right:
- Calculate the width as right - left.
- Calculate the height as the minimum of the elements at indices left and right.
- Calculate the area as width * height.
- Update maxArea to the maximum of maxArea and area.
- Move the pointer with the smaller element (the one that contributes less to the area) inward.
Return maxArea.

Questions to Ask:

Can the input array have negative integers?
Can the input array have duplicate elements?
Can the input array be empty?

JavaScript Solution:

function maxArea(height) {
  let maxArea = 0;
  let left = 0;
  let right = height.length - 1;

  while (left < right) {
    let width = right - left;
    let h = Math.min(height[left], height[right]);
    let area = width * h;
    maxArea = Math.max(maxArea, area);

    if (height[left] < height[right]) {
      left++;
    } else {
      right--;
    }
  }

  return maxArea;
}

Time Complexity - O(N), where N is the number of elements in the height array. Space Complexity - O(1).

Python Solution:

def max_area(height):
    maxArea = 0
    left = 0
    right = len(height) - 1

    while left < right:
        width = right - left
        h = min(height[left], height[right])
        area = width * h
        maxArea = max(maxArea, area)

        if height[left] < height[right]:
            left += 1
        else:
            right -= 1

    return maxArea

Time Complexity - O(N), where N is the number of elements in the height array. Space Complexity - O(1).

3. Fast and Slow Pointers:

Problem 1: Linked List Cycle

Given head, the head of a linked list, determine if the linked list has a cycle in it.

There is a cycle in a linked list if there is some node in the list that can be reached again by continuously following the next pointer. Internally, pos is used to denote the index of the node that tail's next pointer is connected to. Note that pos is not passed as a parameter.

Return true if there is a cycle in the linked list. Otherwise, return false.

Pseudocode:

Initialize slow and fast pointers, both starting at the head of the linked list.
Iterate the pointers as long as fast and fast.next are not null:
- Move slow one step forward.
- Move fast two steps forward.
- If slow and fast meet at the same node, return true (cycle found).
If the loop exits, return false (no cycle).

Questions to Ask:

Can the linked list have duplicate elements?
Can the linked list be empty?
Will the linked list always be singly linked?

JavaScript Solution:

function hasCycle(head) {
  let slow = head;
  let fast = head;

  while (fast && fast.next) {
    slow = slow.next;
    fast = fast.next.next;

    if (slow === fast) {
      return true;
    }
  }

  return false;
}

Time Complexity - O(N), where N is the number of nodes in the linked list. Space Complexity - O(1).

Python Solution:

class ListNode:
    def __init__(self, val=0, next=None):
        self.val = val
        self.next = next

def has_cycle(head):
    slow = head
    fast = head

    while fast and fast.next:
        slow = slow.next
        fast = fast.next.next

        if slow == fast:
            return True

    return False

Time Complexity - O(N), where N is the number of nodes in the linked list. Space Complexity - O(1).

Problem 2: Middle of the Linked List

Given the head of a singly linked list, return the middle node of the linked list.

If there are two middle nodes, return the second middle node.

Pseudocode:

Initialize slow and fast pointers, both starting at the head of the linked list.
Iterate the pointers as long as fast and fast.next are not null:
- Move slow one step forward.
- Move fast two steps forward.
- When fast reaches the end of the linked list, slow will be at the middle node.
Return the value of the middle node.

Questions to Ask:

Can the linked list have duplicate elements?
Can the linked list be empty?
Will the linked list always be singly linked?

JavaScript Solution:

function findMiddle(head) {
  let slow = head;
  let fast = head;

  while (fast && fast.next) {
    slow = slow.next;
    fast = fast.next.next;
  }

  return slow.val;
}

Time Complexity - O(N), where N is the number of nodes in the linked list. Space Complexity - O(1).

Python Solution:

def find_middle(head):
    slow = head
    fast = head

    while fast and fast.next:
        slow = slow.next
        fast = fast.next.next

    return slow.val

Time Complexity - O(N), where N is the number of nodes in the linked list. Space Complexity - O(1).

4. Merge Intervals:

Problem 1: Merge Intervals

Pseudocode:

Sort the intervals based on their start time.
Initialize an empty list, merged, to store the merged intervals.
Iterate through the sorted intervals:
- If the current interval overlaps with the last interval in merged, merge them by updating the end time of the last interval.
- Otherwise, add the current interval to merged.
Return the merged list.

Questions to Ask:

Can the intervals have negative start and end times?
Can the intervals have overlapping ranges?

JavaScript Solution:

function mergeIntervals(intervals) {
  intervals.sort((a, b) => a[0] - b[0]);
  let merged = [intervals[0]];

  for (let i = 1; i < intervals.length; i++) {
    if (intervals[i][0] <= merged[merged.length - 1][1]) {
      merged[merged.length - 1][1] = Math.max(merged[merged.length - 1][1], intervals[i][1]);
    } else {
      merged.push(intervals[i]);
    }
  }

  return merged;
}

Time Complexity - O(N log N), where N is the number of intervals in the array due to sorting. Space Complexity - O(N) in the worst case when all intervals are non-overlapping and need to be merged.

Python Solution:

def merge_intervals(intervals):
    intervals.sort(key=lambda x: x[0])
    merged = [intervals[0]]

    for interval in intervals[1]:
        if interval[0] <= merged[-1][1]:
            merged[-1][1] = max(merged[-1][1], interval[1])
        else:
            merged.append(interval)

    return merged

Time Complexity - O(N log N), where N is the number of intervals in the array due to sorting. Space Complexity - O(N) in the worst case when all intervals are non-overlapping and need to be merged.

#f85565Problem 2: Insert Interval**

Pseudocode:

Initialize an empty list, merged, to store the merged intervals.
Iterate through the intervals:
- If the current interval ends before the new interval starts, add it to merged.
- If the current interval starts after the new interval ends, add the new interval to merged and update it to the current interval.
- Otherwise, merge the intervals by updating the start and end times of the new interval to cover both.
Return the merged list.

Questions to Ask:

Can the intervals have negative start and end times?
Can the intervals have overlapping ranges?

JavaScript Solution:

function insertInterval(intervals, newInterval) {
  let merged = [];
  let i = 0;

  while (i < intervals.length && intervals[i][1] < newInterval[0]) {
    merged.push(intervals[i]);
    i++;
  }

  while (i < intervals.length && intervals[i][0] <= newInterval[1]) {
    newInterval[0] = Math.min(newInterval[0], intervals[i][0]);
    newInterval[1] = Math.max(newInterval[1], intervals[i][1]);
    i++;
  }

  merged.push(newInterval);

  while (i < intervals.length) {
    merged.push(intervals[i]);
    i++;
  }

  return merged;
}

Time Complexity - O(N), where N is the number of intervals in the intervals array. Space Complexity - O(N) in the worst case when all intervals need to be merged.

Python Solution:

def insert_interval(intervals, new_interval):
    merged = []
    i = 0

    while i < len(intervals) and intervals[i][1] < new_interval[0]:
        merged.append(intervals[i])
        i += 1

    while i < len(intervals) and intervals[i][0] <= new_interval[1]:
        new_interval[0] = min(new_interval[0], intervals[i][0])
        new_interval[1] = max(new_interval[1], intervals[i][1])
        i += 1

    merged.append(new_interval)

    while i < len(intervals):
        merged.append(intervals[i])
        i += 1

    return merged

Time Complexity - O(N), where N is the number of intervals in the intervals array. Space Complexity - O(N) in the worst case when all intervals need to be merged.

5. Cyclic Sort:

Problem 1: Find the Missing Number

Pseudocode:

Use the cyclic sort algorithm to place each number in its correct position.
Iterate through the sorted array to find the first missing number (the index that does not have the correct number).
Return the missing number.

Questions to Ask:

Can the input array have negative integers?
Can the input array have duplicate elements?
Will there always be exactly one missing number?

JavaScript Solution:

function findMissingNumber(nums) {
  const n = nums.length;
  for (let i = 0; i < n; i++) {
    while (nums[i] < n && nums[i] !== nums[nums[i]]) {
      [nums[nums[i]], nums[i]] = [nums[i], nums[nums[i]]];
    }
  }

  for (let i = 0; i < n; i++) {
    if (nums[i] !== i) {
      return i;
    }
  }

  return n;
}

Time Complexity - O(N), where N is the number of elements in the nums array. Space Complexity - O(1).

Python Solution:

def find_missing_number(nums):
    n = len(nums)
    for i in range(n):
        while nums[i] < n and nums[i] != nums[nums[i]]:
            nums[nums[i]], nums[i] = nums[i], nums[nums[i]]

    for i in range(n):
        if nums[i] != i:
            return i

    return n

Time Complexity - O(N), where N is the number of elements in the nums array. Space Complexity - O(1).

Problem 2: Find All Missing Numbers

Pseudocode:

Use the cyclic sort algorithm to place each number in its correct position.
Iterate through the sorted array to find all missing numbers (the indices that do not have the correct number).
Return the list of missing numbers.

Questions to Ask:

Can the input array have negative integers?
Can the input array have duplicate elements?
Will there be more than one missing number?

JavaScript Solution:

function findMissingNumbers(nums) {
  const n = nums.length;
  for (let i = 0; i < n; i++) {
    while (nums[i] < n && nums[i] !== nums[nums[i]]) {
      [nums[nums[i]], nums[i]] = [nums[i], nums[nums[i]]];
    }
  }

  const missingNumbers = [];
  for (let i = 0; i < n; i++) {
    if (nums[i] !== i) {
      missingNumbers.push(i);
    }
  }

  return missingNumbers;
}

Time Complexity - O(N), where N is the number of elements in the nums array. Space Complexity - O(1).

Python Solution:

def find_missing_numbers(nums):
    n = len(nums)
    for i in range(n):
        while nums[i] < n and nums[i] != nums[nums[i]]:
            nums[nums[i]], nums[i] = nums[i], nums[nums[i]]

    missing_numbers = []
    for i in range(n):
        if nums[i] != i:
            missing_numbers.append(i)

    return missing_numbers

Time Complexity - O(N), where N is the number of elements in the nums array. Space Complexity - O(1).

<h1In-Place Reversal of Linked List:

6. In-Place Reversal of Linked List

Problem 1: Reverse Linked List

Pseudocode:

Initialize prev as null to keep track of the previous node.
Iterate through the linked list:
- Save the next node in temp.
- Reverse the current node by updating its next to prev.
- Move prev to the current node.
- Move to the next node using temp.
Return prev, which will be the new head of the reversed linked list.

Questions to Ask:

Can the linked list be empty?
Will the linked list always be singly linked?

JavaScript Solution:

function reverseLinkedList(head) {
  let prev = null;
  while (head) {
    let temp = head.next;
    head.next = prev;
    prev = head;
    head = temp;
  }
  return prev;
}

Time Complexity - O(N), where N is the number of nodes in the linked list. Space Complexity - O(1).

Python Solution:

class ListNode:
    def __init__(self, val=0, next=None):
        self.val = val
        self.next = next

def reverse_linked_list(head):
    prev = None
    while head:
        next_node = head.next
        head.next = prev
        prev = head
        head = next_node
    return prev

Time Complexity - O(N), where N is the number of nodes in the linked list. Space Complexity - O(1).

Problem 2: Reverse Linked List II

Pseudocode:

Initialize dummy as a new node with value -1, pointing to the original head.
Initialize prev to dummy, current to head, and reverse_tail to current.
Iterate through the linked list until reaching position m:
- Move prev to current.
- Move current to the next node.
Continue iterating and reverse the nodes from position m to n:
- Save the next node in temp.
- Reverse the current node by updating its next to prev.
- Move prev to the current node.
- Move current to the next node using temp.
Update the pointers to link the reversed section back into the original list.
Return dummy.next, which will be the new head of the linked list.

Questions to Ask:

Can the linked list be empty?
Will m and n always be valid indices within the linked list?

JavaScript Solution:

function reverseLinkedListRange(head, m, n) {
  let dummy = new ListNode(-1);
  dummy.next = head;
  let prev = dummy;

  for (let i = 1; i < m; i++) {
    prev = prev.next;
  }

  let current = prev.next;
  let reverseTail = current;
  let prevNext = null;

  for (let i = m; i <= n; i++) {
    let temp = current.next;
    current.next = prevNext;
    prevNext = current;
    current = temp;
  }

  prev.next = prevNext;
  reverseTail.next = current;

  return dummy.next;
}

Time Complexity - O(N), where N is the number of nodes in the linked list. Space Complexity - O(1).

Python Solution:

class ListNode:
    def __init__(self, val=0, next=None):
        self.val = val
        self.next = next

def reverse_linked_list_range(head, m, n):
    dummy = ListNode(-1)
    dummy.next = head
    prev = dummy

    for _ in range(m - 1):
        prev = prev.next

    current = prev.next
    reverse_tail = current
    prev_next = None

    for _ in range(n - m + 1):
        temp = current.next
        current.next = prev_next
        prev_next = current
        current = temp

    prev.next = prev_next
    reverse_tail.next = current

    return dummy.next

Time Complexity - O(N), where N is the number of nodes in the linked list. Space Complexity - O(1).

7. Tree BFS

Problem 1: Binary Tree Level Order Traversal

Pseudocode:

Initialize an empty list, result, to store the level order traversal.
Initialize a queue, queue, with the root node.
While the queue is not empty:
- Initialize an empty list, currentLevel, to store the nodes at the current level.
- Get the size of the queue (size), which represents the number of nodes at the current level.
- Iterate size times to process all nodes at the current level:
  - Dequeue the front node from the queue.
  - Add the node's value to currentLevel.
  - Enqueue the left and right children of the node if they exist.
- Add currentLevel to the result.
Return the result.

Questions to Ask:

Can the tree have duplicate values?
Can the tree be empty?

JavaScript Solution:

class TreeNode {
  constructor(val = 0, left = null, right = null) {
    this.val = val;
    this.left = left;
    this.right = right;
  }
}

function levelOrderTraversal(root) {
  if (!root) {
    return [];
  }

  let result = [];
  let queue = [root];

  while (queue.length) {
    let currentLevel = [];
    let size = queue.length;

    for (let i = 0; i < size; i++) {
      let node = queue.shift();
      currentLevel.push(node.val);

      if (node.left) {
        queue.push(node.left);
      }
      if (node.right) {
        queue.push(node.right);
      }
    }

    result.push(currentLevel);
  }

  return result;
}

Time Complexity - O(N), where N is the number of nodes in the binary tree. Space Complexity - O(N) in the worst case when the tree is balanced and each level has N/2 nodes in the queue.

Python Solution:

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

def level_order_traversal(root):
    if not root:
        return []

    result = []
    queue = [root]

    while queue:
        current_level = []
        size = len(queue)

        for _ in range(size):
            node = queue.pop(0)
            current_level.append(node.val)

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

        result.append(current_level)

    return result

Time Complexity - O(N), where N is the number of nodes in the binary tree. Space Complexity - O(N) in the worst case when the tree is balanced and each level has N/2 nodes in the queue.

Problem 2: Binary Tree Right Side View

Pseudocode:

Initialize an empty list, result, to store the right side view nodes.
Initialize a queue, queue, with the root node.
While the queue is not empty:
- Initialize currentLevel to an empty list.
- Get the size of the queue (size), which represents the number of nodes at the current level.
- Iterate size times to process all nodes at the current level:
  - Dequeue the front node from the queue.
  - If it is the last node at the current level, add its value to currentLevel.
  - Enqueue the left and right children of the node if they exist.
- Add the last node's value from currentLevel to the result.
Return the result.

Questions to Ask:

Can the tree have duplicate values?
Can the tree be empty?

JavaScript Solution:

class TreeNode {
  constructor(val = 0, left = null, right = null) {
    this.val = val;
    this.left = left;
    this.right = right;
  }
}

function rightSideView(root) {
  if (!root) {
    return [];
  }

  let result = [];
  let queue = [root];

  while (queue.length) {
    let currentLevel = [];
    let size = queue.length;

    for (let i = 0; i < size; i++) {
      let node = queue.shift();
      if (i === size - 1) {
        currentLevel.push(node.val);
      }

      if (node.left) {
        queue.push(node.left);
      }
      if (node.right) {
        queue.push(node.right);
      }
    }

    result.push(currentLevel[currentLevel.length - 1]);
  }

  return result;
}

Time Complexity - O(N), where N is the number of nodes in the binary tree. Space Complexity - O(N) in the worst case when the tree is a complete binary tree and the queue holds all nodes of the last level.

Python Solution:

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

def right_side_view(root):
    if not root:
        return []

    result = []
    queue = [root]

    while queue:
        current_level = []
        size = len(queue)

        for i in range(size):
            node = queue.pop(0)
            if i == size - 1:
                current_level.append(node.val)

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

        result.append(current_level[-1])

    return result

8. Tree DFS

Problem 1: Binary Tree Inorder Traversal

Pseudocode:

Initialize an empty list, result, to store the inorder traversal.
Define a recursive helper function, inorderTraversalRecursive, that takes the current node as an argument:
- If the current node is not null, recursively call the function on the left child.
- Add the current node's value to result.
- If the current node is not null, recursively call the function on the right child.
Call the inorderTraversalRecursive function with the root node.
Return the result.

Questions to Ask:

Can the tree have duplicate values?
Can the tree be empty?

JavaScript Solution:

class TreeNode {
  constructor(val = 0, left = null, right = null) {
    this.val = val;
    this.left = left;
    this.right = right;
  }
}

function inorderTraversal(root) {
  let result = [];

  function inorderTraversalRecursive(node) {
    if (!node) {
      return;
    }
    inorderTraversalRecursive(node.left);
    result.push(node.val);
    inorderTraversalRecursive(node.right);
  }

  inorderTraversalRecursive(root);
  return result;
}

Time Complexity - O(N), where N is the number of nodes in the binary tree. Space Complexity - O(N) for the recursion stack.

Python Solution:

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

def inorder_traversal(root):
    result = []

    def inorder_traversal_recursive(node):
        if not node:
            return
        inorder_traversal_recursive(node.left)
        result.append(node.val)
        inorder_traversal_recursive(node.right)

    inorder_traversal_recursive(root)
    return result

Time Complexity - O(N), where N is the number of nodes in the binary tree. Space Complexity - O(N) for the recursion stack.

Problem 2: Binary Tree Maximum Path Sum

Pseudocode:

Define a recursive helper function, maxPathSumRecursive, that takes the current node as an argument:
- If the current node is null, return 0.
- Recursively find the maximum path sum for the left and right subtrees using maxPathSumRecursive.
- Calculate the local maximum path sum for the current node as the maximum value between:
  - The current node's value.
  - The sum of the current node's value and the maximum path sum from the left subtree (if positive).
  - The sum of the current node's value and the maximum path sum from the right subtree (if positive).
- Update the maxSum as the maximum value between the current maximum path sum and the local maximum path sum.
- Return the local maximum path sum.
Initialize maxSum to a minimum integer value.
Call the maxPathSumRecursive function with the root node.
Return maxSum.

Questions to Ask:

Can the tree have negative values?
Can the tree have duplicate values?
Can the tree be empty?

JavaScript Solution:

class TreeNode {
  constructor(val = 0, left = null, right = null) {
    this.val = val;
    this.left = left;
    this.right = right;
  }
}

function maxPathSum(root) {
  let maxSum = Number.MIN_SAFE_INTEGER;

  function maxPathSumRecursive(node) {
    if (!node) {
      return 0;
    }

    let leftSum = Math.max(0, maxPathSumRecursive(node.left));
    let rightSum = Math.max(0, maxPathSumRecursive(node.right));
    let localSum = node.val + leftSum + rightSum;

    maxSum = Math.max(maxSum, localSum);

    return node.val + Math.max(leftSum, rightSum);
  }

  maxPathSumRecursive(root);
  return maxSum;
}

Time Complexity - O(N), where N is the number of nodes in the binary tree. Space Complexity - O(H), where H is the height of the binary tree for the recursion stack.

Python Solution:

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

def max_path_sum(root):
    max_sum = float("-inf")

    def max_path_sum_recursive(node):
        nonlocal max_sum
        if not node:
            return 0

        left_sum = max(0, max_path_sum_recursive(node.left))
        right_sum = max(0, max_path_sum_recursive(node.right))
        local_sum = node.val + left_sum + right_sum

        max_sum = max(max_sum, local_sum)

        return node.val + max(left_sum, right_sum)

    max_path_sum_recursive(root)
    return max_sum

Time Complexity - O(N), where N is the number of nodes in the binary tree. Space Complexity - O(H), where H is the height of the binary tree for the recursion stack.

9. Two Heaps

Problem: Find the Median of a Number Stream

Pseudocode:

Initialize a min heap, minHeap, to store the larger half of the numbers.

10. Subsets:

Problem 1: Subsets

#Pseudocode:

Initialize an empty list, result, to store the subsets.
Define a recursive helper function, backtrack, that takes the current index and the current subset as arguments.
Add the current subset to the result.
Iterate from the current index to the end of the nums array:
- Include the current element in the subset.
- Recursively call backtrack with the next index and the updated subset.
- Exclude the current element from the subset.
Call the backtrack function with index 0 and an empty subset.
Return the result.

#Questions to Ask:

Can the nums array have duplicate elements?
Can the nums array be empty?

#JavaScript Solution:

function subsets(nums) {
  let result = [];

  function backtrack(index, currentSubset) {
    result.push([...currentSubset]);

    for (let i = index; i < nums.length; i++) {
      currentSubset.push(nums[i]);
      backtrack(i + 1, currentSubset);
      currentSubset.pop();
    }
  }

  backtrack(0, []);
  return result;
}

#Python Solution:

def subsets(nums):
    result = []

    def backtrack(index, current_subset):
        result.append(current_subset[])

        for i in range(index, len(nums)):
            current_subset.append(nums[i])
            backtrack(i + 1, current_subset)
            current_subset.pop()

    backtrack(0, [])
    return result

Time Complexity - O(2^N), where N is the number of elements in the nums array, as each element can either be included or excluded in the subsets. Space Complexity: O(2^N) for the result list.

Problem 2: Subsets II

#Pseudocode:

Initialize an empty list, result, to store the subsets.
Define a recursive helper function, backtrack, that takes the current index and the current subset as arguments.
Add the current subset to the result.
Iterate from the current index to the end of the nums array:
- If the current element is the same as the previous element and not the first element:
  - Skip to the next element to avoid duplicate subsets.
- Include the current element in the subset.
- Recursively call backtrack with the next index and the updated subset.
- Exclude the current element from the subset.
Call the backtrack function with index 0 and an empty subset.
Return the result.

#Questions to Ask:

Can the nums array have duplicate elements?
Can the nums array be empty?

#JavaScript Solution:

function subsetsWithDup(nums) {
  let result = [];

  function backtrack(index, currentSubset) {
    result.push([...currentSubset]);

    for (let i = index; i < nums.length; i++) {
      if (i > index && nums[i] === nums[i - 1]) {
        continue;
      }
      currentSubset.push(nums[i]);
      backtrack(i + 1, currentSubset);
      currentSubset.pop();
    }
  }

  nums.sort((a, b) => a - b);
  backtrack(0, []);
  return result;
}

#Python Solution:

def subsetsWithDup(nums):
    result = []

    def backtrack(index, current_subset):
        result.append(current_subset[])

        for i in range(index, len(nums)):
            if i > index and nums[i] == nums[i - 1]:
                continue
            current_subset.append(nums[i])
            backtrack(i + 1, current_subset)
            current_subset.pop()

    nums.sort()
    backtrack(0, [])
    return result

11. Modified Binary Search:

Problem 1: Find Minimum in Rotated Sorted Array

#Pseudocode:

Initialize left to 0 and right to the last index of the array.
While left is less than right:
- Calculate the middle index as (left + right) / 2.
- If the middle element is greater than the last element (rightmost element):
  - Update left to mid + 1.
- Otherwise, update right to mid.
Return the element at index left as the minimum element.

#JavaScript Solution:

function findMin(nums) {
  let left = 0;
  let right = nums.length - 1;

  while (left < right) {
    let mid = Math.floor((left + right) / 2);

    if (nums[mid] > nums[right]) {
      left = mid + 1;
    } else {
      right = mid;
    }
  }

  return nums[left];
}

#Python Solution:

def find_min(nums):
    left = 0
    right = len(nums) - 1

    while left < right:
        mid = (left + right) // 2

        if nums[mid] > nums[right]:
            left = mid + 1
        else:
            right = mid

    return nums[left]

Time Complexity - O(log N), where N is the number of elements in the array. Space Complexity: O(1).

Problem 2: Search in Rotated Sorted Array

#Pseudocode:

Initialize left to 0 and right to the last index of the array.
While left is less than or equal to right:
- Calculate the middle index as (left + right) / 2.
- If the middle element is equal to the target, return its index.
- If the left half (from left to mid) is sorted:
  - If the target is within the left half, update right to mid - 1.
  - Otherwise, update left to mid + 1.
- If the right half (from mid to right) is sorted:
  - If the target is within the right half, update left to mid + 1.
  - Otherwise, update right to mid - 1.
Return -1, indicating that the target element is not found.

#JavaScript Solution:

function search(nums, target) {
  let left = 0;
  let right = nums.length - 1;

  while (left <= right) {
    let mid = Math.floor((left + right) / 2);

    if (nums[mid] === target) {
      return mid;
    }

    if (nums[left] <= nums[mid])

#JavaScript Solution (Continued):

    if (nums[left] <= nums[mid]) {
      if (target >= nums[left] && target < nums[mid]) {
        right = mid - 1;
      } else {
        left = mid + 1;
      }
    } else {
      if (target > nums[mid] && target <= nums[right]) {
        left = mid + 1;
      } else {
        right = mid - 1;
      }
    }
  }

  return -1;
}

#Python Solution:

def search(nums, target):
    left = 0
    right = len(nums) - 1

    while left <= right:
        mid = (left + right) // 2

        if nums[mid] == target:
            return mid

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

    return -1

Time Complexity - O(log N), where N is the number of elements in the array. Space Complexity: O(1).

12. Top K Elements

Problem 1: Kth Largest Element in an Array

#Pseudocode:

Sort the input array in descending order.
Return the element at the (k - 1) index, which represents the kth largest element.

#JavaScript Solution:

function findKthLargest(nums, k) {
  nums.sort((a, b) => b - a);
  return nums[k - 1];
}

#Python Solution:

def find_kth_largest(nums, k):
    nums.sort(reverse=True)
    return nums[k - 1]

Time Complexity - O(N log N), where N is the number of elements in the array due to the sorting step. Space Complexity: O(1) (in-place sorting).

Problem 2: Top K Frequent Elements

#Pseudocode:

Create a frequency map to store the count of each element in the input array.
Create a list of tuples where each tuple contains the element and its corresponding frequency.
Sort the list of tuples based on the frequency in descending order.
Return the first k elements from the sorted list.

#JavaScript Solution:

function topKFrequent(nums, k) {
  let frequencyMap = new Map();
  for (let num of nums) {
    frequencyMap.set(num, (frequencyMap.get(num) || 0) + 1);
  }

  let sortedEntries = [...frequencyMap.entries()].sort((a, b) => b[1] - a[1]);
  let result = [];
  for (let i = 0; i < k; i++) {
    result.push(sortedEntries[i][0]);
  }
  return result;
}

#Python Solution:

def top_k_frequent(nums, k):
    frequency_map = {}
    for num in nums:
        frequency_map[num] = frequency_map.get(num, 0) + 1

    sorted_entries = sorted(frequency_map.items(), key=lambda x: x[1], reverse=True)
    return [entry[0] for entry in sorted_entries[:k]]

Time Complexity - O(N log N) for sorting, where N is the number of elements in the array. Space Complexity: O(N) for the frequency map.

13. K-way Merge

Problem 1: Merge k Sorted Lists

#Pseudocode:

Create a min heap and insert the first node from each list into the heap.
Initialize a dummy head node and a current pointer.
While the heap is not empty:
- Pop the smallest node from the heap.
- Append the popped node to the current pointer.
- Move the current pointer to the next node in the merged list.
- If the popped node has a next node, insert the next node into the heap.
Return the next node of the dummy head, which represents the head of the merged list.

#JavaScript Solution:

class ListNode {
  constructor(val = 0, next = null) {
    this.val = val;
    this.next = next;
  }
}

function mergeKLists(lists) {
  let heap = new MinHeap();

  for (let list of lists) {
    if (list) {
      heap.insert(list);
    }
  }

  let dummyHead = new ListNode();
  let current = dummyHead;

  while (!heap.isEmpty()) {
    let node = heap.extractMin();
    current.next = node;
    current = current.next;

    if (node.next) {
      heap.insert(node.next);
    }
  }

  return dummyHead.next;
}

class MinHeap {
  constructor() {
    this.heap = [];
  }

  insert(node) {
    this.heap.push(node);
    this.bubbleUp(this.heap.length - 1);
  }

  bubbleUp(index) {
    while (index > 0) {
      let parentIndex = Math.floor((index - 1) / 2);
      if (this.heap[parentIndex].val <= this.heap[index].val) {
        break;
      }
      [this.heap[parentIndex], this.heap[index]] = [this.heap[index], this.heap[parentIndex]];
      index = parentIndex;
    }
  }

  extractMin() {
    if (this.isEmpty()) {
      return null;
    }

    let minNode = this.heap[0];
    let lastNode = this.heap.pop();
    if (!this.isEmpty()) {
      this.heap[0] = lastNode;
      this.bubbleDown(0);
    }
    return minNode;
  }

  bubbleDown(index) {
    let left = 2 * index + 1;
    let right = 2 * index + 2;
    let smallest = index;

    if (left < this.heap.length && this.heap[left].val < this.heap[smallest].val) {
      smallest = left;
    }

    if (right < this.heap.length && this.heap[right].val < this.heap[smallest].val) {
      smallest = right;
    }

    if (smallest !== index) {
      [this.heap[index], this.heap[smallest]] = [this.heap[smallest], this.heap[index]];
      this.bubbleDown(smallest);
    }
  }

  isEmpty() {
    return this.heap.length === 0;
  }
}

#Python Solution:

class ListNode:
    def __init__(self, val=0, next=None):
        self.val = val
        self.next = next

def merge_k_lists(lists):
    import heapq

    heap = []
    for list_node in lists:
        if list_node:
            heapq.heappush(heap, (list_node.val, list_node))

    dummy_head = ListNode()
    current = dummy_head

    while heap:
        val, node = heapq.heappop(heap)
        current.next = node
        current = current.next
        if node.next:
            heapq.heappush(heap, (node.next.val, node.next))

    return dummy_head.next

#JavaScript Solution Explanation:

We define a ListNode class to represent a node in the linked list.
The mergeKLists function takes a list of linked lists lists as input and returns the head of the merged sorted list.
We create a MinHeap class to implement a min heap data structure, where the smallest node is always at the top.
In the mergeKLists function, we create an instance of the MinHeap class.
We insert the first node from each linked list into the heap.
We create a dummy head node and a current pointer to keep track of the merged list.
While the heap is not empty:
- We extract the smallest node from the heap (the root of the heap).
- We append the extracted node to the current pointer.
- If the extracted node has a next node, we insert the next node into the heap.
- We move the current pointer to the next node in the merged list.
Finally, we return the head of the merged list (the next node of the dummy head).

#Python Solution Explanation:

We import the heapq module to use a priority queue as a min heap.
The merge_k_lists function takes a list of linked lists lists as input and returns the head of the merged sorted list.
We create an empty heap to store tuples of (val, node) where val is the value of the node and node is the node itself.
We push the first node from each linked list into the heap with their values as priorities.
We create a dummy head node and a current pointer to keep track of the merged list.
While the heap is not empty:
- We extract the tuple with the smallest value (the root of the heap).
- We assign the node from the tuple to the current.next.
- If the extracted node has a next node, we push the next node into the heap with its value as priority.
- We move the current pointer to the next node in the merged list.
Finally, we return the head of the merged list (the next node of the dummy head).

Time Complexity - O(N log k), where N is the total number of nodes across all lists and k is the number of lists. The heap operations (insertion and extraction) take O(log k) time, and we perform them N times. Space Complexity: O(k), where k is the number of lists. The heap can contain at most k nodes at any time.

Problem 2: Merge Intervals

Given a collection of intervals, merge overlapping intervals.

Example:

Input: intervals = [[1, 3], [2, 6], [8, 10], [15, 18]] Output: [[1, 6], [8, 10], [15, 18]]

#Pseudocode:

Sort the intervals based on the start time in ascending order.
Initialize an empty list merged to store the merged intervals.
Iterate through the sorted intervals:
- If merged is empty or the current interval's start is greater than the end of the last interval in merged, append the current interval to merged.
- Otherwise, update the end time of the last interval in merged if the end time of the current interval is greater.
Return the merged list.

#Questions to Ask:

Can the input intervals be empty?
Can the input intervals contain negative values?
Can the input intervals have duplicate intervals?

#JavaScript Solution:

function merge(intervals) {
  if (!intervals || intervals.length === 0) {
    return [];
  }

  intervals.sort((a, b) => a[0] - b[0]);
  let merged = [intervals[0]];

  for (let i = 1; i < intervals.length; i++) {
    let currentInterval = intervals[i];
    let lastMerged = merged[merged.length - 1];

    if (currentInterval[0] > lastMerged[1]) {
      merged.push(currentInterval);
    } else {
      lastMerged[1] = Math.max(lastMerged[1], currentInterval[1]);
    }
  }

  return merged;
}

#Python Solution:

def merge(intervals):
    if not intervals:
        return []

    intervals.sort(key=lambda x: x[0])
    merged = [intervals[0]]

    for interval in intervals[1]:
        current_start, current_end = interval
        last_start, last_end = merged[-1]

        if current_start > last_end:
            merged.append(interval)
        else:
            merged[-1] = [last_start, max(current_end, last_end)]

    return merged

Time Complexity - O(N log N) for sorting, where N is the number of intervals. The merging process takes O(N) time as we iterate through the sorted intervals only once. Space Complexity -O(N) for the merged list.

14. Topological Sort

Problem: Course Schedule

Pseudocode:

Initialize an empty list, result, to store the topological order of courses.
Initialize a dictionary, inDegrees, to keep track of the indegree (number of incoming edges) for each course.
Initialize a queue, queue, with courses that have an indegree of 0.
While the queue is not empty:
- Dequeue a course from the queue and add it to the result.
- Iterate through the prerequisites for the dequeued course:
  - Decrement the indegree of the prerequisite course.
  - If the indegree becomes 0, enqueue the prerequisite course to the queue.
Check if the number of courses in the result is equal to the total number of courses. If not, it means there is a cycle, and we cannot complete all courses.
Return the result.

Questions to Ask:

Can there be duplicate courses?
Can the prerequisites list have duplicate entries?

JavaScript Solution:

function canFinish(numCourses, prerequisites) {
  let result = [];
  let inDegrees = {};
  let graph = {};

  for (let [course, prereq] of prerequisites) {
    if (!graph[prereq]) graph[prereq] = [];
    graph[prereq].push(course);

    inDegrees[course] = inDegrees[course] ? inDegrees[course] + 1 : 1;
    if (!inDegrees[prereq]) inDegrees[prereq] = 0;
  }

  let queue = [];
  for (let course in inDegrees) {
    if (inDegrees[course] === 0) queue.push(course);
  }

  while (queue.length) {
    let currCourse = queue.shift();
    result.push(currCourse);

    if (graph[currCourse]) {
      for (let nextCourse of graph[currCourse]) {
        inDegrees[nextCourse]--;
        if (inDegrees[nextCourse] === 0) queue.push(nextCourse);
      }
    }
  }

  return result.length === numCourses;
}

Time Complexity - O(N + E), where N is the number of courses and E is the number of prerequisites. Space Complexity - O(N + E), for the inDegrees and graph dictionaries, and the queue.

Python Solution:

def can_finish(num_courses, prerequisites):
    result = []
    in_degrees = {}
    graph = {}

    for course, prereq in prerequisites:
        if prereq not in graph:
            graph[prereq] = []
        graph[prereq].append(course)

        in_degrees[course] = in_degrees.get(course, 0) + 1
        in_degrees[prereq] = in_degrees.get(prereq, 0)

    queue = []
    for course in in_degrees:
        if in_degrees[course] == 0:
            queue.append(course)

    while queue:
        curr_course = queue.pop(0)
        result.append(curr_course)

        if curr_course in graph:
            for next_course in graph[curr_course]:
                in_degrees[next_course] -= 1
                if in_degrees[next_course] == 0:
                    queue.append(next_course)

    return len(result) == num_courses

Time Complexity - O(N + E), where N is the number of courses and E is the number of prerequisites. Space Complexity - O(N + E), for the in_degrees and graph dictionaries, and the queue.

Preparing for Problems:

Read and understand the problem statement carefully. Make sure to identify the input, output, and constraints.
Clarify any doubts by asking questions about the problem.
Identify the appropriate algorithm or data structure that can be used to solve the problem efficiently.
Break down the problem into smaller sub-problems and try to solve each sub-problem step by step.
Write the# pseudocode for the solution to the sub-problems and then combine them to form the final solution.
Test the# pseudocode with different test cases to verify its correctness.
Once the# pseudocode is correct, implement the solution in the desired programming language (JavaScript or Python).
Test the code thoroughly with various test cases to ensure it works as expected.
Analyze the time and space complexity of the solution to assess its efficiency.
Optimize the solution if possible by reducing the time or space complexity or improving the code readability.
Document the code properly with comments to explain the logic and any important steps.
Submit the solution and discuss the approach and code with others to gain insights and learn from different perspectives.

Conclusion:

I hope this article was helpful in understanding the process of solving coding problems. I have tried to explain the approach in detail with examples and# pseudocode. I have also provided the JavaScript and# Python solutions for each problem. I would recommend you to try solving the problems on your own before looking at the solutions. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!

mathcodes/dsa_pattern.md

14 Coding Patterns To Master

Table of Contents

1. Sliding Window

2. Longest Substring Without Repeating Characters:

2. Two Pointers or Iterators:

Problem 1: Two Sum

Pseudocode:

Questions to Ask:

JavaScript Solution:

Python Solution:

Problem 2: Container With Most Water

Pseudocode:

Questions to Ask:

JavaScript Solution:

Python Solution:

3. Fast and Slow Pointers:

Problem 1: Linked List Cycle

Pseudocode:

Questions to Ask:

JavaScript Solution:

Python Solution:

Problem 2: Middle of the Linked List

Pseudocode:

Questions to Ask:

JavaScript Solution:

Python Solution:

4. Merge Intervals:

Problem 1: Merge Intervals

Pseudocode:

Questions to Ask:

JavaScript Solution:

Python Solution:

#f85565Problem 2: Insert Interval**

Pseudocode:

Questions to Ask:

JavaScript Solution:

Python Solution:

5. Cyclic Sort:

Problem 1: Find the Missing Number

Pseudocode:

Questions to Ask:

JavaScript Solution:

Python Solution:

Problem 2: Find All Missing Numbers

Pseudocode:

Questions to Ask:

JavaScript Solution:

Python Solution:

6. In-Place Reversal of Linked List

Problem 1: Reverse Linked List

Pseudocode:

Questions to Ask:

JavaScript Solution:

Python Solution:

Problem 2: Reverse Linked List II

Pseudocode:

Questions to Ask:

JavaScript Solution:

Python Solution:

7. Tree BFS

Problem 1: Binary Tree Level Order Traversal

Pseudocode:

Questions to Ask:

JavaScript Solution:

Python Solution:

Problem 2: Binary Tree Right Side View

Pseudocode:

Questions to Ask:

JavaScript Solution:

Python Solution:

8. Tree DFS

Problem 1: Binary Tree Inorder Traversal

Pseudocode:

Questions to Ask:

JavaScript Solution:

Python Solution:

Problem 2: Binary Tree Maximum Path Sum

Pseudocode:

Questions to Ask: