LeetCode 8 Patterns

LeetCode 8 Patterns

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🧠 Two Pointers Pattern

🔹 Why Two Pointers Matter

• Optimizes problems involving linear data structures (arrays, strings, linked lists).
• Reduces time complexity from brute-force O(n²) to efficient O(n).
• Useful in situations where you’d otherwise compare every combination of elements.

🔹 Types of Two Pointer Techniques

1. Same Direction

• Pointers move left to right together.
• Ideal for scanning or processing data in a single pass.
• Example: Fast & Slow Pointers
  • Detecting cycles in linked lists.
  • Finding the middle of a list.
  • Slow pointer: moves 1 step.
  • Fast pointer: moves 2 steps.

2. Opposite Directions

• One pointer starts at the beginning, the other at the end.
• Common in sorted arrays or for pair-finding tasks.
• Example: Two Sum Problem
  • Find two numbers that add to a target.
  • Adjust pointers inward based on current sum.
  • Efficiently eliminates unnecessary comparisons.

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🧠 Sliding Window Pattern

🔹 What It Is

• A specialized form of the two pointers technique.
• Focuses on maintaining a dynamic range (window) of elements.
• Used to analyze contiguous subarrays or substrings.
• Adjusts window size dynamically as the data is processed.

🔹 How It Works

1. Two pointers define the window: One marks the start, one marks the end.
2. Expand: As the end pointer moves forward, the window expands to include more elements.
3. Shrink: As conditions are violated or goals are met, the start pointer moves forward to shrink the window.

🔹 Key Use Cases

• Finding maximum/minimum of something in a subarray.
• Tracking running sums, averages, or character counts.
• Searching for longest/shortest substrings meeting certain criteria.

🔹 Example Problem: Longest substring without repeating characters

1. Expand the window by moving the end pointer.
2. If a duplicate character appears, move the start pointer forward until the duplicate is removed.
3. Use a hashmap to track characters inside the window.

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🧠 Binary Search

🔹 What It Is

• A search algorithm used to find a target in a sorted array.
• Variant of the two pointers technique using a left and right boundary.
• Time complexity: O(log n) — far more efficient than linear search O(n).

🔹 How It Works

1. Initialize two pointers: left at the start, right at the end.
2. Find the middle: mid = (left + right) // 2.
3. Compare array[mid] with target:
   • If equal → return success.
   • If mid < target → search the right half.
   • If mid > target → search the left half.
4. Repeat until pointers converge or element is found.

🔹 More Than Just Numbers

Binary Search also works for:

• Monotonic functions: Consistently increasing or decreasing sequences.
• True/False patterns: When a condition changes from false to true in a list (e.g., finding the first True in a list of booleans).

🔹 Creative Use Case: Minimum in Rotated Sorted Array

• Even though it seems unsorted, a monotonic condition exists.
• If element < last element → it’s in the rotated section.
• If element > last element → it’s in the original sorted section.

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🧠 Breadth-First Search (BFS)

🔹 What It Is

• Algorithm for exploring graphs or trees level by level.
• Starts from a given node and visits all immediate neighbors before moving to the next level.

🔹 Key Characteristics

• Data Structure: Queue (FIFO - First In, First Out).
• Tracks Visited Nodes: To prevent infinite loops.
• Ideal for: Shortest path in unweighted graphs, Level-order traversal.

🔹 BFS Template Overview

1. Initialize a queue and a visited set.
2. Add starting node to queue.
3. While queue is not empty:
   • Remove front node.
   • Add to result.
   • Add unvisited neighbors to queue.

🔹 Example: Level-Order Traversal

Input Tree: 3 /
9 20 /
15 7

Output: [[3], [9, 20], [15, 7]]

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🧠 Depth-First Search (DFS)

🔹 What It Is

• Traversal algorithm that explores as far down a branch as possible before backtracking.
• Contrast: BFS goes level-by-level (Queue); DFS dives deep (Stack/Recursion).

🔹 Key Characteristics

• Data Structure: Stack (or call stack via recursion).
• Ideal for: Exploring all paths, cycle detection, backtracking.

🔹 DFS Template (Recursive)

    def dfs(node, visited):
        if not node or node in visited:
            return
        visited.add(node)
        for neighbor in node.neighbors:
            dfs(neighbor, visited)

🔹 Example Problem: Number of Islands

• Goal: Count connected groups of ‘1’s in a grid.
• Approach: Iterate through grid. If ‘1’ is found, increment count and run DFS to mark all connected ‘1’s as ‘0’ (visited).

    def numIslands(grid):
        if not grid:
            return 0
        rows, cols = len(grid), len(grid[0])
        count = 0
  
        def dfs(r, c):
            if r < 0 or c < 0 or r >= rows or c >= cols or grid[r][c] == '0':
                return
            grid[r][c] = '0' # Mark as visited
            dfs(r+1, c)
            dfs(r-1, c)
            dfs(r, c+1)
            dfs(r, c-1)
  
        for r in range(rows):
            for c in range(cols):
                if grid[r][c] == '1':
                    dfs(r, c)
                    count += 1
        return count
  

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🔁 Backtracking

🔹 What Is Backtracking?

• Extension of DFS.
• Builds the solution space dynamically.
• Used in combinatorial problems where the full decision tree isn’t known upfront.

🔹 Key Concepts

• Explore options one decision at a time.
• If a dead end or invalid path is reached, backtrack (undo last decision) and try another path.

🔹 Template Pattern

    def backtrack(path, options):
        if end_condition(path):
            result.append(path[:])
            return
        for option in options:
            if is_valid(option):
                path.append(option)
                backtrack(path, updated_options)
                path.pop()  # backtrack

🔹 Example: Letter Combinations of Phone Number

Input: “23” -> Output: [“ad”, “ae”, “af”, “bd”, “be”, “bf”, “cd”, “ce”, “cf”]

    def letterCombinations(digits):
        if not digits:
            return []

        digit_map = {
            "2": "abc", "3": "def", "4": "ghi", "5": "jkl",
            "6": "mno", "7": "pqrs", "8": "tuv", "9": "wxyz"
        }
        result = []

        def dfs(index, path):
            if index == len(digits):
                result.append("".join(path))
                return
            for char in digit_map[digits[index]]:
                path.append(char)
                dfs(index + 1, path)
                path.pop()  # backtrack

        dfs(0, [])
        return result

• Time Complexity: O(4ⁿ) (Exponential).

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⬆️ Priority Queue (Heap)

🔹 When to Use

• Top K elements.
• K smallest or K largest values.

🔹 Min vs Max Heap

• Min Heap: Smallest element at root.
• Max Heap: Largest element at root.

🔹 The Counterintuitive Rule

• Find K Smallest → Use Max Heap.
  • Why? Keep heap size at K. If a new number is smaller than the largest (root), pop the root and insert the new one.
• Find K Largest → Use Min Heap.

🔹 Efficiency

• Access root: O(1)
• Insert/Remove: O(log n)

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📊 Dynamic Programming (DP)

🔹 Core Idea

• Break problem into overlapping subproblems.
• Solve each subproblem once and store the result (Memoization).

🔹 Two Main Approaches

1. Top-Down (Memoization)

• Start from main problem, recurse down.
• Store results in dictionary/array.
• Essentially: Backtracking + Caching.

2. Bottom-Up (Tabulation)

• Solve smallest subproblems first.
• Build up to larger problems using loops.
• Often more efficient (no recursion overhead).

🔹 Summary

• DP optimizes exponential brute-force solutions to polynomial time.
• Fundamental for problems involving optimal decisions, counting, and partitioning.