Master fundamental and advanced algorithms essential for technical interviews at top companies. This category covers sorting algorithms, search algorithms, graph algorithms, and optimization techniques that form the foundation of computer science problem-solving.
What You’ll Learn:
Sorting algorithms: Quick sort, merge sort, heap sort
Search techniques: Binary search and variations
Graph algorithms: DFS, BFS, Dijkstra’s, topological sort
Optimization: Dynamic programming, greedy algorithms
Time and space complexity analysis
Difficulty Progression: Start with basic sorting and searching, then progress to graph algorithms and dynamic programming.
Interview Frequency: Extremely high – algorithms appear in 80%+ of technical interviews at Google, Amazon, Facebook, Microsoft, and Apple.
7 min read Stock trading dynamic programming problems are a classic interview cluster. Every variant – one transaction, unlimited, at most k, cooldown, […] Read article
9 min read What is Interval DP? Interval DP is a dynamic programming pattern where you optimize over all sub-intervals of a range. Read article
10 min read 2D DP Pattern Overview 2D dynamic programming problems use a 2D table where dp[i][j] represents the optimal value considering only Read article
10 min read Advanced Heap Interview Patterns Heaps appear in a surprising number of interview problems beyond the simple “find the Kth largest Read article
9 min read Advanced Binary Search Interview Patterns Binary search is not just “is target in sorted array.” The real interview value comes Read article
9 min read Graph Traversal Interview Patterns Graph problems appear in nearly every senior engineering interview. The key is recognizing which traversal fits Read article
9 min read Dynamic Programming on Strings Interview Patterns String DP problems share a common structure: a 2D table where one dimension indexes Read article
8 min read Core Pattern Tree DP follows a single structural pattern: post-order DFS. You recurse into children first, then compute the answer Read article
10 min read Linked list problems trip up candidates because pointer manipulation is easy to mess up under pressure. These patterns cover every Read article
10 min read Sorting problems appear constantly in interviews – not just “implement merge sort” but as tools to simplify harder problems. Master Read article
8 min read When Greedy Works: The Exchange Argument Greedy algorithms make the locally optimal choice at each step. They are provably correct Read article
9 min read Strings appear in roughly 25-30% of coding interviews. Unlike arrays or trees, string problems have a concentrated set of patterns Read article
7 min read Interval problems are a reliable interview topic at every level. The core patterns repeat across dozens of problems – learn Read article
9 min read Trie (prefix tree) problems appear frequently in coding interviews. This guide covers the core patterns with Python implementations you can Read article
11 min read Shortest path problems appear in nearly every graph interview. The key skill is recognizing which algorithm applies to the given Read article
