Project Euler Conquest Path

A structured roadmap to solve challenging mathematical and programming problems from Project Euler.

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Phase Main Topic Content (Typical Problems) Goal
1 Warm-up & Familiarization (1-25)
  • Problem 1, 2, 3, 5, 10
 Get familiar with the platform, solve simple problems.
2 Basic Number Theory (26-50)
  • Problem 27, 31, 35, 41, 48
 Master basic concepts of prime numbers and modular arithmetic.
3 Combinatorics & Probability (51-75)
  • Problem 53, 65, 71, 72
 Apply basic counting principles and probability.
4 Advanced Number Theory (76-100)
  • Problem 76, 81, 87, 97, 100
 Solve more complex problems related to number theory.
5 Algorithms & Data Structures (101-125)
  • Problem 102, 107, 112, 119
 Use basic algorithms and data structures.
6 Cryptography & Congruence (126-150)
  • Problem 129, 132, 134, 145
 Delve deeper into congruence concepts and applications.
7 Geometry & Pathfinding
  • Problem 153, 162, 82
 Solve geometry and pathfinding problems.
8 Advanced Combinatorics (176-200)
  • Problem 179, 187, 191, 200
 Master complex combinatorics and counting techniques.
9 Game Theory
  • Problem 301, 299, 238
 Understand and apply basic principles of game theory.
10 Large Number Handling
  • Problem 20, 25, 55, 56
 Work efficiently with numbers exceeding standard data type limits.
11 Optimization & Search
  • Problem 114, 115, 103, 166
 Develop efficient search and optimization algorithms.
12 Peak Challenges (201+)
  • Problem 205, 206, 357, 203
 Solve the hardest problems requiring a combination of multiple techniques.

Core Mindsets for Project Euler

1. Math First, Code Second

These are math problems disguised as programming challenges. Spend most of your time with a pen and paper understanding the mathematics before you write any code.

2. Brute Force is a Starting Point

A naive, brute-force solution is a great way to understand the problem and test small cases. The real challenge is finding the elegant, efficient algorithm that solves it within the one-minute rule.

3. Research is a Skill

You are not expected to know everything. Learning to identify the required mathematical concept (e.g., "this involves Euler's totient function") and researching it is part of the problem-solving process.

4. Efficiency is Everything

The 60-second rule is a hard constraint. This forces you to think deeply about algorithmic complexity. An O(n²) solution is rarely good enough when N is large.