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Algorithms Core Notes

A single-page, at-a-glance revision project for core algorithms and pattern-based problem solving.

This project is designed as a fast reference and structured summary sheet covering essential algorithm concepts without unnecessary depth.
It focuses on clarity, time and space trade-offs, correctness thinking, and interview-ready patterns using clean JavaScript examples.

alt text

Purpose

  • Quick revision before interviews
  • Rapid recall of core algorithm patterns
  • Strong mental model of time complexity and space complexity
  • Practical, decision-driven reminders for choosing the right approach
  • Clear JavaScript implementations that prioritize logic over language tricks

Coverage

Foundations (Non-negotiable)

  • What is an algorithm
  • Input → process → output model
  • Correctness vs efficiency
  • Complexity recap (time and space)
  • Big O refresher (brief but clear)
  • Common growth rates intuition
  • Recursion mental model and stack cost
  • Iterative vs recursive trade-offs
  • Amortized analysis (quick intro)
  • Lower bounds (conceptual)

Searching

  • Linear search
  • Binary search
  • Binary search variations (first/last occurrence, lower/upper bound)
  • Search on answer concept (intro)
  • Two pointer search idea (when applicable)

Sorting

  • Bubble sort
  • Selection sort
  • Insertion sort
  • Merge sort
  • Quick sort
  • Heap sort (concept + usage)
  • Counting sort (when constraints allow)
  • Stable vs unstable sorting
  • In-place vs extra memory sorting

Recursion and Backtracking

  • Base case thinking
  • Subsets generation
  • Permutations generation
  • Combination patterns
  • Backtracking pruning concept
  • Divide and conquer concept

Two Pointers and Sliding Window

  • Two pointers basics
  • Opposite ends pointers pattern
  • Fast and slow pointers (cycle detection idea)
  • Sliding window fixed size
  • Sliding window variable size
  • Common window problems (sum, max, distinct count concept)

Stack and Queue Patterns

  • Stack fundamentals for “undo / nesting”
  • Valid parentheses
  • Next greater element concept
  • Monotonic stack (intro)
  • Queue for BFS mindset
  • Deque usage patterns (intro)

Greedy Algorithms

  • Greedy choice property (intuition)
  • Interval scheduling concept
  • Two pointers + greedy combinations
  • When greedy fails (quick warning)
  • Greedy vs DP boundary (intro)

Dynamic Programming (DP)

  • Overlapping subproblems and optimal substructure
  • Memoization (top-down)
  • Tabulation (bottom-up)
  • 1D DP patterns (Fibonacci style)
  • 2D DP intuition (grid style intro)
  • 0/1 knapsack concept
  • Longest increasing subsequence (conceptual)
  • DP state design checklist

Graph Algorithms (Core)

  • Graph representations (adjacency list, matrix)
  • BFS
  • DFS
  • Connected components idea
  • Cycle detection concept
  • Topological sort concept
  • Shortest path intro (BFS vs Dijkstra idea)
  • Union-Find usage concept (for cycle/MST idea)

Complexity Master Section

  • Worst case vs average case
  • Time vs space trade-offs
  • When Big O hides constants
  • Practical performance mistakes (nested loops, repeated scanning, heavy sorting)
  • Choosing the right approach under constraints

Tech Stack

  • React
  • Vite
  • styled-components

Project Type

Single page only
Section-based navigation
Searchable and expandable content
No blog-style content, only structured notes

Each section is modular and collapsible for fast scanning.

Run Locally

npm install
npm run dev