DSA Mastery

Linked Lists: Singly, Doubly, and Circular

1 Views Updated 5/4/2026

Linked Lists Mastery

Unlike arrays, a Linked List does not use contiguous memory. Each element (Node) is a separate object on the Heap that contains a value and a Pointer to the next node. This makes insertions and deletions extremely efficient (O(1)) at the cost of slow random access (O(N)).

1. Types of Linked Lists

  • Singly Linked: Each node points only to the 'Next' node. (Low memory, one-way travel).
  • Doubly Linked: Each node points to both 'Next' and 'Previous'. (Higher memory, two-way travel). This is what .NET's LinkedList<T> uses.
  • Circular Linked: The last node points back to the first node. (Used for round-robin scheduling).

2. When to use a Linked List?

Use it when you need to frequently add/remove items from the **Start** or **Middle** of a collection. In an array, adding to the start requires shifting every other element (O(N)). In a Linked List, you just swap two pointers (O(1)).

4. Interview Mastery

Q: "How do you detect a Cycle (Loop) in a Singly Linked List?"

Architect Answer: "We use **Floyd's Cycle-Finding Algorithm** (also known as the Tortoise and the Hare). We have two pointers. The Slow pointer moves 1 step; the Fast pointer moves 2 steps. If there is a cycle, the Fast pointer will eventually 'lap' the Slow pointer and they will meet. If the Fast pointer hits NULL, there is no cycle. It is an O(N) time and O(1) space solution."

DSA Mastery
1. Algorithmic Foundations
Big O Notation: Analyzing Time and Space Complexity Memory Management: Stack vs Heap in C# Recursion: The foundation of modern algorithms
2. Linear Data Structures
Arrays Deep Dive: Static vs Dynamic (List<T> Internals) Linked Lists: Singly, Doubly, and Circular Stacks and Queues: Implementing Undo/Redo & Message Buffers Hash Tables: Handling Collisions like a Pro
3. Non-Linear Data Structures
Binary Trees & BST: Searching at Log(N) speed Balanced Trees: AVL and Red-Black Trees Internals Heaps: Implementing a Priority Queue Tries (Prefix Trees): Optimizing Auto-complete features Graphs (Part 1): Representation (Matrix vs List) Graphs (Part 2): BFS vs DFS Traversal
4. Searching & Sorting
Binary Search: The power of Divide & Conquer Elementary Sorts: Bubble, Selection, and Insertion Merge Sort: Stable sorting for massive datasets Quick Sort: In-place sorting and Pivot selection Heap Sort: Leveraging the Priority Queue
5. Algorithmic Patterns
Sliding Window Pattern: Optimizing array performance Two Pointers Pattern: Reversing and Finding cycles Fast & Slow Pointers (Hare & Tortoise) Backtracking: Solving Sudoku and N-Queens
6. Dynamic Programming (DP)
Memoization vs Tabulation: Top-down vs Bottom-up The Knapsack Problem: 0/1 DP optimization Longest Common Subsequence (LCS) Matrix Chain Multiplication
7. Advanced Graphs & Interview
Dijkstra's Algorithm: Shortest path in weighted graphs Prim's and Kruskal's: Minimum Spanning Trees Disjoint Set Union (DSU) / Union-Find DSA Interview: FAANG Style Coding Challenges