Effective Steps to Solve LeetCode Problems

Steps to Solve LeetCode Problems Effectively

1. Understand the Problem (5-10 minutes)

• Read carefully: Grasp the problem, constraints, and examples.
• Clarify edge cases: Think about boundaries and special cases.
• Restate the problem: Rephrase in your own words for clarity.

2. Identify the Core Algorithm or Data Structure (5-10 minutes)

• Consider which algorithm or data structure applies (e.g., sliding window, DP, graphs).
• Think about simplifications or reducing the problem into subproblems.

3. Plan the Solution (10-15 minutes)

• Write pseudocode: Draft your logic step-by-step.
• Choose an approach: Start with brute force; optimize later.
• Validate: Ensure your plan fits the constraints.

4. Implement the Solution (30-45 minutes)

• Translate pseudocode into code.
• Test incrementally with provided and custom edge cases.
• Focus on correctness first.

5. Analyze and Optimize (10-15 minutes)

• Check complexity: Evaluate time and space efficiency.
• Refactor code: Simplify or improve readability.
• Compare with optimal solutions for better insights.

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When to Check the Solution

• Easy problems: After ~30-45 minutes.
• Medium problems: After ~60 minutes.
• Hard problems: After ~90 minutes.
• If stuck despite multiple approaches, review the solution.

How to Learn from Solutions

1. Understand the optimal approach: Study and compare with your attempt.
2. Re-implement: Write the solution yourself without copying.
3. Reflect: Note key patterns or techniques for future problems.

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Tips to Save Time

• Focus on patterns: Practice similar problems to build intuition.
• Avoid overthinking: Start coding with a basic approach and iterate.
• Set goals: Track progress with a weekly problem-solving target.
• Use hints sparingly: Use LeetCode hints before checking full solutions.

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Algorithms and Data Structures to Consider

Core Algorithms

1. Two Pointers and Sliding Window

   • Ideal for array or string problems, especially when searching for subarrays or substrings.

2. Binary Search

   • Useful for searching in sorted arrays or minimizing/maximizing problems.

3. Dynamic Programming

   • For problems with overlapping subproblems and optimal substructure properties.

4. Graph Algorithms

   • Breadth-First Search (BFS), Depth-First Search (DFS), Dijkstra’s algorithm for shortest paths.

5. Backtracking

   • Perfect for generating permutations, subsets, or solving constraint-based problems.

6. Greedy Algorithms

   • Suitable for problems that can be solved by local optimization.

7. Divide and Conquer

   • For recursively breaking problems into smaller subproblems (e.g., merge sort).

8. Union-Find

   • Useful for connected components or cycle detection in graphs.

9. Topological Sort

   • For dependency resolution in directed acyclic graphs (DAGs).

Core Data Structures

1. Arrays and Strings

   • Basics for almost every problem.

2. Hash Tables and Sets

   • For constant-time lookups or checking for duplicates.

3. Stacks and Queues

   • For problems involving LIFO/FIFO ordering, parenthesis matching, or sliding windows.

4. Heaps/Priority Queues

   • For efficient retrieval of minimum or maximum elements.

5. Linked Lists

   • For traversal or manipulation of nodes.

6. Trees

   • Binary trees, binary search trees, and traversal techniques (in-order, pre-order, post-order).

7. Graphs

   • Adjacency list and matrix representations, traversal techniques.

8. Tries

   • For prefix searches or dictionary problems.

9. Bit Manipulation

   • For problems involving binary representations or XOR logic.

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By referring to this list when stuck, you can quickly identify potential techniques or structures to tackle the problem. Over time, you’ll naturally recognize which approach to use based on the problem type.