Dynamic Programming Optimization Patterns: Space Reduction, Bitmask DP, and Interval DP (2025)
5 min read 1D DP Space Optimization # Fibonacci: O(n) -> O(1) space def fib(n: int) -> int: if n int: prev2 = […] Read article
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.
5 min read 1D DP Space Optimization # Fibonacci: O(n) -> O(1) space def fib(n: int) -> int: if n int: prev2 = […] Read article
6 min read Binary Indexed Tree (Fenwick Tree / BIT) A BIT supports prefix sum queries and point updates in O(log n) time Read article
6 min read Dijkstra’s Algorithm – Shortest Path (Non-negative Weights) import heapq from collections import defaultdict def dijkstra(graph: dict, start: int, n: int) Read article
4 min read Heap Fundamentals A heap is a complete binary tree where every parent satisfies the heap property (min-heap: parent = children). Read article
4 min read GCD, LCM, and Euclidean Algorithm import math def gcd(a: int, b: int) -> int: while b: a, b = b, Read article
6 min read Recursion Fundamentals Every recursive solution has: a base case (stops the recursion), a recursive case (reduces the problem), and a Read article
5 min read String Fundamentals for Interviews Strings are immutable in Python and Java — concatenating in a loop is O(n^2). Use a Read article
7 min read Trie Fundamentals and Implementation A trie (prefix tree) stores strings character by character, sharing prefixes among strings. Each node represents Read article
6 min read Binary Search Fundamentals Binary search finds a target in a sorted collection in O(log n). The key insight: at each Read article
6 min read Union-Find Data Structure Union-Find (also called Disjoint Set Union, DSU) tracks which elements belong to the same connected component. Supports Read article
6 min read The Monotonic Stack Pattern A monotonic stack maintains elements in strictly increasing or strictly decreasing order. When a new element Read article
5 min read Stack: LIFO and Its Applications A stack (Last In, First Out) is the go-to structure when you need to process Read article
6 min read The Two Pointers Pattern Two pointers uses two indices that move toward each other (or in the same direction) to Read article
6 min read The Sliding Window Pattern Sliding window optimizes O(n^2) brute-force approaches on contiguous subarrays/substrings to O(n). The key insight: instead of Read article
7 min read Why Bit Manipulation Matters in Interviews Bit manipulation problems test your understanding of how integers are represented in binary and Read article