Summary of LeetCode Was Hard Until I Learned THESE 8 Patterns (With Templates!)

Main Ideas and Concepts

• Patterns Overview
  • Linear Patterns: For data structures like arrays, linked lists, and strings.
  • Nonlinear Patterns: For data structures like trees and graphs.
• Linear Patterns
  • Two Pointers
    • Reduces time complexity by allowing traversal in linear time (O(n)).
    • Two methods:
      • Same Direction: Used for single-pass problems (e.g., detecting cycles in linked lists).
      • Opposite Directions: Used for finding pairs (e.g., two numbers that sum to a target).
  • Sliding Window
    • An extension of the two-pointer technique that maintains a dynamic range of elements.
    • Useful for problems involving substrings or subarrays (e.g., finding the longest substring without repeating characters).
  • Binary Search
    • Efficiently finds a target in a sorted array using a divide-and-conquer strategy (O(log n)).
    • Applicable to any monotonic function, not just sorted numbers.
  • Heaps
    • Used for finding top K elements (K smallest or largest).
    • Min Heap for K largest values; Max Heap for K smallest values.
• Nonlinear Patterns
  • Breadth-First Search (BFS)
    • Explores nodes level by level in trees or graphs using a queue.
    • Ideal for problems requiring level order traversal (e.g., level order traversal of a binary tree).
  • Depth-First Search (DFS)
    • Explores as far down a branch as possible before backtracking.
    • Suitable for problems that require exploring all paths (e.g., counting islands in a grid).
  • Backtracking
    • An extension of DFS, exploring all possible solutions by making decisions and backtracking when necessary (e.g., generating letter combinations from phone numbers).
  • Dynamic Programming (DP)
    • Involves optimizing solutions by breaking them down into overlapping subproblems.
    • Two approaches: Top-Down (recursion with memorization) and Bottom-Up (iterative table filling).

Methodologies and Instructions

• Two Pointers:
  • Use Two Pointers moving in the same direction for single-pass problems.
  • Use Two Pointers moving in opposite directions for pair finding.
• Sliding Window:
  • Maintain a start and end pointer to dynamically adjust the window size based on conditions.
• Binary Search:
  • Start with left and right pointers, checking the middle element and adjusting based on comparisons.
• BFS:
  • Use a queue to process nodes level by level.
• DFS:
  • Use a stack (or recursion) to explore one path as deeply as possible before backtracking.
• Backtracking:
  • Explore potential solutions by making decisions and undoing them when hitting dead ends.
• Dynamic Programming:
  • Use memorization for top-down approaches; fill out tables for bottom-up approaches.

Featured Speakers/Sources

• The video references "Algo Monster" for additional resources and templates related to the patterns discussed.

This summary encapsulates the key takeaways from the video, providing a structured overview of the patterns and methodologies useful for coding interviews.

Notable Quotes

— 17:40 — « One thing that is counterintuitive is that we use a Min Heap to find the K largest values and a Max Heap to find the K smallest values. »

— 19:14 — « Dynamic programming or DP is considered the hardest pattern to learn. »

— 19:55 — « Memorization is just a fancy word for a simple concept. It means saving the previous function call result in a dictionary and reading from it when we do the exact same call again. »

Category

Educational

Video