· 6 min read

LeetCode Algorithms Overview for Senior Developers

Master the essential algorithms and data structures patterns needed to excel in technical interviews and solve complex coding problems efficiently.

Core Algorithm Patterns

Array & String Manipulation

Two Pointers

  • Pattern: Use two pointers moving from different ends or at different speeds
  • Use Cases: Palindrome checking, removing duplicates, finding pairs
  • Examples: Two Sum, Valid Palindrome, Container With Most Water
  • Time Complexity: O(n)
  • Space Complexity: O(1)

Sliding Window

  • Pattern: Maintain a window of elements that slides through the array
  • Use Cases: Substring problems, maximum/minimum in subarray
  • Examples: Longest Substring Without Repeating Characters, Maximum Sum Subarray
  • Time Complexity: O(n)
  • Space Complexity: O(1) to O(k) where k is window size

Prefix Sum

  • Pattern: Precompute cumulative sums to answer range queries quickly
  • Use Cases: Range sum queries, subarray problems
  • Examples: Range Sum Query, Subarray Sum Equals K
  • Time Complexity: O(n) preprocessing, O(1) query
  • Space Complexity: O(n)

Kadane’s Algorithm

  • Pattern: Find maximum sum subarray using dynamic programming
  • Use Cases: Maximum subarray problems, stock trading
  • Examples: Maximum Subarray, Best Time to Buy and Sell Stock
  • Time Complexity: O(n)
  • Space Complexity: O(1)

Linked List Operations

Reverse Linked List

  • Pattern: Iteratively or recursively reverse node pointers
  • Use Cases: Palindrome checking, list manipulation
  • Examples: Reverse Linked List, Palindrome Linked List
  • Time Complexity: O(n)
  • Space Complexity: O(1) iterative, O(n) recursive

Fast and Slow Pointers (Floyd’s Cycle Detection)

  • Pattern: Use two pointers moving at different speeds
  • Use Cases: Cycle detection, finding middle element
  • Examples: Linked List Cycle, Middle of the Linked List
  • Time Complexity: O(n)
  • Space Complexity: O(1)

Merge Linked Lists

  • Pattern: Combine two sorted linked lists
  • Use Cases: Merge sort, combining sorted data
  • Examples: Merge Two Sorted Lists, Merge k Sorted Lists
  • Time Complexity: O(n + m)
  • Space Complexity: O(1)

Tree Traversal & Manipulation

Binary Tree Traversal

  • Pattern: Visit all nodes in specific order
  • Types: Preorder, Inorder, Postorder, Level-order (BFS)
  • Use Cases: Tree serialization, validation, path problems
  • Examples: Binary Tree Inorder Traversal, Serialize and Deserialize Binary Tree
  • Time Complexity: O(n)
  • Space Complexity: O(h) where h is height

Binary Search Tree (BST) Operations

  • Pattern: Leverage BST properties for efficient search/insert/delete
  • Use Cases: Search, insertion, deletion, validation
  • Examples: Validate Binary Search Tree, Lowest Common Ancestor
  • Time Complexity: O(log n) average, O(n) worst
  • Space Complexity: O(h)

Tree Path Problems

  • Pattern: Find paths, sum paths, or validate paths in trees
  • Use Cases: Path sum, path counting, path validation
  • Examples: Path Sum, Binary Tree Maximum Path Sum
  • Time Complexity: O(n)
  • Space Complexity: O(h)

Graph Algorithms

Depth-First Search (DFS)

  • Pattern: Explore as far as possible before backtracking
  • Use Cases: Path finding, cycle detection, topological sort
  • Examples: Number of Islands, Course Schedule, Word Search
  • Time Complexity: O(V + E)
  • Space Complexity: O(V)

Breadth-First Search (BFS)

  • Pattern: Explore level by level using a queue
  • Use Cases: Shortest path, level-order traversal, minimum steps
  • Examples: Binary Tree Level Order Traversal, Word Ladder
  • Time Complexity: O(V + E)
  • Space Complexity: O(V)

Union-Find (Disjoint Set)

  • Pattern: Efficiently track and merge disjoint sets
  • Use Cases: Connected components, cycle detection in undirected graphs
  • Examples: Number of Connected Components, Redundant Connection
  • Time Complexity: O(α(n)) amortized
  • Space Complexity: O(n)

Dynamic Programming

1D Dynamic Programming

  • Pattern: Solve problems by breaking them into smaller subproblems
  • Use Cases: Optimization problems, counting problems
  • Examples: Climbing Stairs, House Robber, Longest Increasing Subsequence
  • Time Complexity: O(n)
  • Space Complexity: O(n) or O(1) with optimization

2D Dynamic Programming

  • Pattern: Use 2D table to store results of subproblems
  • Use Cases: Grid problems, string matching, knapsack problems
  • Examples: Unique Paths, Edit Distance, Longest Common Subsequence
  • Time Complexity: O(m × n)
  • Space Complexity: O(m × n) or O(min(m, n)) with optimization

Memoization

  • Pattern: Cache results of expensive function calls
  • Use Cases: Recursive problems with overlapping subproblems
  • Examples: Fibonacci, Word Break, Coin Change
  • Time Complexity: O(n) to O(n²)
  • Space Complexity: O(n)

Sorting & Searching

Binary Search

  • Pattern: Search in sorted array by repeatedly dividing search space
  • Use Cases: Finding elements, insertion points, peak finding
  • Examples: Search in Rotated Sorted Array, Find Peak Element
  • Time Complexity: O(log n)
  • Space Complexity: O(1)

Quick Sort & Merge Sort

  • Pattern: Divide and conquer sorting algorithms
  • Use Cases: Sorting arrays, finding kth largest element
  • Examples: Sort an Array, Kth Largest Element in an Array
  • Time Complexity: O(n log n) average
  • Space Complexity: O(log n) to O(n)

Heap Operations

  • Pattern: Use heap data structure for priority-based operations
  • Use Cases: Finding kth largest/smallest, merging sorted arrays
  • Examples: Kth Largest Element, Merge k Sorted Lists
  • Time Complexity: O(n log k)
  • Space Complexity: O(k)

Hash Table & Set Operations

Hash Map/Dictionary

  • Pattern: Use key-value pairs for O(1) lookup
  • Use Cases: Frequency counting, caching, lookups
  • Examples: Two Sum, Group Anagrams, Longest Substring Without Repeating Characters
  • Time Complexity: O(1) average
  • Space Complexity: O(n)

Set Operations

  • Pattern: Use sets for unique elements and fast membership testing
  • Use Cases: Duplicate detection, intersection/union operations
  • Examples: Contains Duplicate, Intersection of Two Arrays
  • Time Complexity: O(1) average
  • Space Complexity: O(n)

String Algorithms

String Matching

  • Pattern: Find patterns within strings efficiently
  • Algorithms: KMP, Rabin-Karp, Z-algorithm
  • Use Cases: Pattern searching, string validation
  • Examples: Implement strStr(), Repeated Substring Pattern
  • Time Complexity: O(n + m)
  • Space Complexity: O(m)

Anagram Problems

  • Pattern: Compare character frequencies or sort strings
  • Use Cases: Grouping anagrams, anagram detection
  • Examples: Group Anagrams, Valid Anagram
  • Time Complexity: O(n log n) or O(n)
  • Space Complexity: O(n)

Bit Manipulation

Bitwise Operations

  • Pattern: Use bit operations for efficient computation
  • Operations: AND, OR, XOR, NOT, left/right shift
  • Use Cases: Counting bits, finding unique numbers, power of 2
  • Examples: Single Number, Number of 1 Bits, Power of Two
  • Time Complexity: O(1) to O(n)
  • Space Complexity: O(1)

XOR Properties

  • Pattern: Leverage XOR properties for problem solving
  • Properties: a ⊕ a = 0, a ⊕ 0 = a, commutative and associative
  • Use Cases: Finding unique elements, missing numbers
  • Examples: Single Number, Missing Number
  • Time Complexity: O(n)
  • Space Complexity: O(1)

Problem-Solving Strategy

1. Understand the Problem

  • Read the problem statement carefully
  • Identify input/output format
  • Ask clarifying questions
  • Consider edge cases

2. Choose the Right Approach

  • Identify the pattern from the problem type
  • Consider time and space complexity constraints
  • Think about brute force first, then optimize
  • Consider multiple approaches

3. Implement the Solution

  • Start with pseudocode
  • Implement step by step
  • Test with examples
  • Handle edge cases

4. Optimize and Refactor

  • Analyze time and space complexity
  • Look for optimization opportunities
  • Clean up the code
  • Add comments for clarity

Common Interview Patterns

Easy Problems (1-2 patterns)

  • Array manipulation
  • String operations
  • Basic tree traversal
  • Simple hash table usage

Medium Problems (2-3 patterns)

  • Combination of multiple patterns
  • Dynamic programming
  • Graph traversal
  • Advanced tree operations

Medium Problems (2-3 patterns)

  • Combination of multiple patterns
  • Dynamic programming
  • Graph traversal
  • Advanced tree operations

Practice Strategy

Phase 1: Learn Patterns (2-3 weeks)

  • Focus on understanding each pattern
  • Solve 3-5 problems per pattern
  • Don’t worry about speed initially

Phase 2: Pattern Recognition (2-3 weeks)

  • Practice identifying patterns quickly
  • Solve problems without looking at solutions
  • Time yourself on easy/medium problems

Phase 3: Speed and Accuracy (2-3 weeks)

  • Focus on solving problems quickly
  • Practice mock interviews
  • Work on medium problems

Phase 4: Interview Preparation (1-2 weeks)

  • Practice explaining your thought process
  • Work on communication skills
  • Review common follow-up questions

Essential LeetCode Problems by Category

Arrays & Strings

  • Two Sum (Easy)
  • Valid Parentheses (Easy)
  • Longest Substring Without Repeating Characters (Medium)
  • Container With Most Water (Medium)
  • 3Sum (Medium)

Linked Lists

  • Reverse Linked List (Easy)
  • Merge Two Sorted Lists (Easy)
  • Linked List Cycle (Easy)
  • Copy List with Random Pointer (Medium)
  • Remove Nth Node From End of List (Medium)

Trees

  • Maximum Depth of Binary Tree (Easy)
  • Validate Binary Search Tree (Medium)
  • Binary Tree Level Order Traversal (Medium)
  • Lowest Common Ancestor of a Binary Tree (Medium)
  • Path Sum (Easy)

Dynamic Programming

  • Climbing Stairs (Easy)
  • House Robber (Easy)
  • Longest Increasing Subsequence (Medium)
  • Word Break (Medium)
  • Coin Change (Medium)

Graphs

  • Number of Islands (Medium)
  • Course Schedule (Medium)
  • Redundant Connection (Medium)
  • Find the Town Judge (Easy)
  • Find All Paths From Source to Target (Medium)

Conclusion

Mastering these core algorithms and patterns will give you the foundation to solve most LeetCode problems. Remember:

  • Practice regularly - Consistency is key
  • Understand the patterns - Don’t just memorize solutions
  • Time yourself - Speed matters in interviews
  • Explain your approach - Communication is crucial
  • Learn from mistakes - Review and understand wrong approaches

Start with the patterns you’re least familiar with, practice systematically, and gradually build up your problem-solving skills. With dedication and the right approach, you’ll be ready to tackle any coding interview challenge.

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