Coding Interview Patterns for Linear and Nonlinear Data Structures

Introduction

• Focus on the eight most important coding interview patterns for linear and nonlinear data structures.
• Patterns are categorized into linear (arrays, linked lists, strings) and nonlinear (trees, graphs).
• Use of pre-built code templates for these patterns.

Linear Data Structures

Two-Pointers Pattern

• Purpose: Reduces time complexity in linear structures (arrays, linked lists, strings).
• Methods:
  • Same Direction: Used for processing or scanning data in a single pass (e.g., fast and slow pointers for cycle detection in linked lists).
  • Opposite Directions: For problems like finding pairs in sorted arrays.

Sliding Window Pattern

• Extension of Two-Pointers: Manages a range or subset of elements, adjusting dynamically.
• Use Cases: Problems requiring tracking contiguous sequences (subarrays/substrings).
• Often combined with hash maps.

Binary Search

• Fundamental Search Algorithm: Finds target value in sorted arrays.
• Pattern: Left and right pointers to halve the list.
• Applications: Beyond sorted numbers, applicable to any list with a monotonic function.
• Example: Minimum in Rotated Sorted Array showcases binary search on a non-obviously sorted structure.

Nonlinear Data Structures

Breadth-First Search (BFS)

• Traversal Method: Level by level in graphs or trees.
• Data Structure: Queue to track visited nodes.
• Template Use: Solve problems like Level Order Traversal of a Binary Tree.

Depth-First Search (DFS)

• Traversal Method: Deep into one path then backtracking.
• Data Structure: Stack, often the call stack in recursive implementations.
• Use Cases: Exploring all paths, checking deep conditions, detecting cycles.
• Example: Counting islands in a 2D grid.

Backtracking

• Extension of DFS: Build solution trees for combinatorial problems.
• Process: Explore all options, backtrack on dead ends.
• Example: Letter combinations of a phone number using a recursive approach.

Heaps and Priority Queues

• Purpose: Solve top k, k smallest/largest questions.
• Types:
  • Min Heap: Smallest value at root.
  • Max Heap: Largest value at root.
• Efficiency: O(log n) for insertions/removals.

Dynamic Programming (DP)

• Complex Pattern: Optimizes by breaking down into overlapping subproblems.
• Approaches:
  • Top-Down (Memoization): Recursive solving with memoization.
  • Bottom-Up: Iterative, usually table-based solutions.
• Requires understanding dependencies between subproblems.

For more in-depth content and practice problems, refer to AlgoMonster's resources and templates.