LeetCode was HARD until I Learned these 15 Patterns
Short Summary:
This video focuses on mastering LeetCode by learning common problem-solving patterns rather than simply memorizing solutions. The speaker, having solved over 1500 LeetCode problems, identifies 15 crucial patterns frequently appearing in interviews at companies like Amazon and Google. These patterns cover various data structures and algorithms, including arrays, linked lists, trees, graphs, and dynamic programming. The video explains each pattern, provides examples, and suggests relevant LeetCode problems for practice. The implications are faster problem-solving, improved interview performance, and a deeper understanding of fundamental computer science concepts. Specific methods like prefix sums, two-pointers, sliding window, fast and slow pointers, monotonic stack, and backtracking are explained in detail.
Detailed Summary:
The video is structured around 15 problem-solving patterns for LeetCode and coding interviews. Each section covers a pattern, its application, an example, and relevant LeetCode practice problems.
-
Prefix Sum: This pattern optimizes subarray sum queries from O(n*m) to O(1) by pre-calculating cumulative sums. The speaker explains how to use a prefix sum array (or sometimes the input array itself) to efficiently answer multiple sum queries.
-
Two Pointers: This technique uses two pointers to traverse a data structure (often an array or string) from opposite ends or in a coordinated manner. The example given is palindrome checking, showing how two pointers can reduce time complexity from O(n²) to O(n).
-
Sliding Window: This pattern efficiently finds subarrays or substrings meeting specific criteria. The example involves finding the maximum sum subarray of a given size K, demonstrating how a sliding window reduces complexity from O(n*K) to O(n).
-
Fast and Slow Pointers: Used primarily with linked lists, this pattern employs two pointers moving at different speeds to detect cycles or find the middle of a linked list. The analogy of two runners on a circular track is used to illustrate the concept.
-
Linked List In-Place Reversal: This pattern focuses on reversing a linked list without using extra space. The speaker explains the algorithm using three pointers (previous, current, next) to efficiently reverse the links.
-
Monotonic Stack: This pattern uses a stack to find the next greater or smaller element in an array in O(n) time, improving upon a naive O(n²) approach. The speaker explains how to maintain a monotonically increasing or decreasing stack.
-
Top K Elements: This pattern addresses finding the K largest/smallest elements efficiently. The speaker suggests using a min-heap (for largest) or max-heap (for smallest), reducing complexity from O(n log n) to O(n log K). Quickselect is mentioned as an alternative.
-
Overlapping Intervals: This pattern handles problems involving merging, intersecting, or inserting intervals. The example focuses on merging overlapping intervals by sorting and iteratively merging adjacent intervals.
-
Modified Binary Search: This pattern adapts binary search for scenarios where the array isn't perfectly sorted (e.g., rotated sorted arrays, arrays with duplicates). The speaker explains how to adjust the search conditions based on the array's properties.
-
Binary Tree Traversal: This section covers pre-order, in-order, post-order, and level-order traversals, explaining their applications and implementation using recursion and iteration.
-
Depth-First Search (DFS): This graph traversal algorithm explores all paths from a starting node. The speaker outlines a general approach and mentions applications like cycle detection and topological sorting.
-
Breadth-First Search (BFS): This graph traversal algorithm explores nodes level by level. The speaker outlines a general approach and mentions applications like finding shortest paths in unweighted graphs.
-
Matrix Traversal: This pattern treats matrices as graphs, applying DFS or BFS to solve problems like finding islands in a grid. The island counting problem is used as an example.
-
Backtracking: This technique explores all possible solution paths, backtracking when a path is invalid. The example given is generating all subsets of an array.
-
Dynamic Programming (DP): This powerful optimization technique is briefly introduced, mentioning common DP patterns like Fibonacci numbers, knapsack, and longest common subsequence, along with relevant LeetCode problems. The speaker suggests a separate blog post for a more detailed explanation.
The speaker consistently emphasizes practicing these patterns with LeetCode problems and encourages viewers to subscribe for future videos and blog posts. The overall message is that understanding these patterns is far more valuable than simply solving a large number of LeetCode problems.