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Problem Analysis

Systematic approach to understanding and breaking down algorithmic problems before implementation.

Understanding requirements​

  • Read Carefully: Understand input/output format and constraints
  • Identify Key Information: Extract important details and edge cases
  • Clarify Ambiguities: Ask questions about unclear requirements
  • Examples: Work through provided examples to understand expected behavior

Identifying patterns​

  • Data Structure Patterns: Recognize when to use arrays, trees, graphs, etc.
  • Algorithm Patterns: Identify common algorithmic approaches (DP, greedy, etc.)
  • Problem Categories: Classify problems by type (search, optimization, etc.)
  • Similar Problems: Connect to previously solved similar problems

Choosing data structures​

  • Access Patterns: Consider how data will be accessed (random, sequential)
  • Operations: Identify required operations (insert, delete, search, update)
  • Constraints: Consider memory and time constraints
  • Trade-offs: Balance between time and space complexity

Time/space tradeoffs​

  • Time vs Space: Understand when to optimize for time vs space
  • Preprocessing: Consider preprocessing data for faster queries
  • Caching: Use memoization or caching for repeated calculations
  • Approximation: Consider approximate solutions for better performance

Edge case handling​

  • Empty Input: Handle empty arrays, strings, or null inputs
  • Single Element: Consider arrays/strings with one element
  • Boundary Values: Handle minimum and maximum possible values
  • Invalid Input: Handle malformed or unexpected input formats