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.
Sorting Algorithm Interview Patterns While most interviews don’t ask you to implement sorting from scratch, understanding the algorithms helps you […]
Tree Interview Patterns Trees appear in nearly every software engineering interview. Mastering the core traversal patterns and recognizing which technique
1D Dynamic Programming Interview Patterns 1D DP problems have a state space that reduces to a single index or value.
Graph BFS and DFS Interview Patterns Graph traversal is one of the most tested interview topics. BFS (Breadth-First Search) and
Trie (Prefix Tree) Interview Patterns A Trie (prefix tree) is a tree where each node represents a character in a
Segment Tree and Fenwick Tree (BIT) Interview Patterns These data structures answer range queries (sum, min, max) and support point
Backtracking Algorithm Patterns Backtracking is a systematic way to enumerate all possible solutions by building candidates incrementally and abandoning (pruning)
Advanced Graph Algorithms for Interviews Beyond BFS/DFS and basic shortest paths, interviews at senior levels test Tarjan’s algorithm for SCCs,
Shortest Path Algorithms in Interviews Shortest path problems ask: given a graph with weighted or unweighted edges, what is the
Advanced Dynamic Programming Patterns Beyond the foundational DP patterns (Fibonacci, 0/1 knapsack, LCS), advanced interviews test state machine DP, interval
String Algorithm Patterns in Interviews String problems are ubiquitous in coding interviews. Most string challenges reduce to a small set
What Is Union-Find? Union-Find (Disjoint Set Union, DSU) is a data structure that tracks a collection of elements partitioned into
What Are Greedy Algorithms? A greedy algorithm makes the locally optimal choice at each step, never backtracking, hoping that local
Recursion and Memoization Interview Patterns Memoization transforms exponential recursive solutions into polynomial ones by caching subproblem results. In Python, @lru_cache
Interval DP and Advanced Dynamic Programming Patterns Interval DP solves optimization problems over contiguous subarrays or subsequences. The core idea:
