Skip to main content

How to Solve LeetCode’s Hard Problems Without Getting Stuck



LeetCode’s hard problems can seem daunting, especially when you’re just getting comfortable with coding. Tackling these problems isn’t about innate talent—it’s about persistence, learning, and applying the right strategies. In this blog, I’ll share actionable tips and techniques to help you solve LeetCode hard problems without getting stuck and make consistent progress in your problem-solving journey.


Why Are Hard Problems Hard?

LeetCode hard problems challenge your understanding of advanced data structures, algorithms, and optimization techniques. Here’s why they’re tough:

  • They require deep problem-solving skills and out-of-the-box thinking.
  • Many involve multiple layers of complexity.
  • Edge cases and large inputs often test your algorithm’s efficiency.
  • They push you to master topics like dynamic programming, graph traversal, and segment trees.

However, with the right approach, you can break down even the most complex problems into manageable parts.


Start with the Right Mindset

Approaching hard problems requires patience and a problem-solving mindset. Here’s how to prepare yourself:

  • Embrace Failure: It’s okay if you don’t solve a problem on the first attempt. Every failure is an opportunity to learn.
  • Focus on Learning: Shift your goal from “solving the problem” to “understanding the problem.”
  • Be Consistent: Tackle hard problems regularly to build confidence and familiarity with complex concepts.

Break the Problem into Smaller Parts

Most hard problems can be decomposed into smaller, more manageable subproblems. Here’s how to do it:

  • Identify the Core Task: What is the problem fundamentally asking you to do? (e.g., find the shortest path, maximize a sum, etc.)
  • Break It Down: Divide the problem into smaller chunks (e.g., preprocessing data, exploring possibilities, calculating results).
  • Focus on One Part at a Time: Solve each chunk independently before combining them.

Start with a Brute-Force Approach

Even if you know brute-force isn’t the final solution, it’s a great way to understand the problem. Here's why:

  • Clarifies Logic: Brute-force helps you grasp the problem’s requirements.
  • Reveals Patterns: While implementing brute-force, you may notice patterns that lead to an optimized solution.
  • Builds a Baseline: You can compare your optimized approach against the brute-force one.

Optimize Step by Step

Once you’ve implemented a brute-force solution, look for ways to improve it:

  • Analyze Time and Space Complexity: Identify bottlenecks in your solution.
  • Apply Data Structures: Use hashmaps, heaps, or tries to optimize operations.
  • Use Algorithms: Incorporate efficient algorithms like binary search, dynamic programming, or divide-and-conquer.

For example:

  • A brute-force approach to finding all pairs with a specific sum can be optimized using a hashmap for constant-time lookups.

Learn and Apply Problem-Solving Patterns

Many LeetCode hard problems are variations of common patterns. Master these patterns to solve problems faster:

  • Sliding Window: For problems involving subarrays or substrings.
  • Dynamic Programming: For optimization problems like knapsack or longest subsequence.
  • Graph Traversal: For problems involving connectivity or shortest paths.
  • Backtracking: For combinations, permutations, and constraint-based problems.

Study Solutions After Trying

If you’re stuck, it’s okay to look at the solution—but do it strategically:

  • Understand the Approach: Don’t just memorize the code. Understand why it works and how the logic is applied.
  • Identify Gaps in Knowledge: Note the concepts or techniques you didn’t know and study them.
  • Re-implement the Solution: After understanding the approach, code it yourself to internalize the logic.

Use Debugging and Visualization Tools

Debugging and visualization tools can help you understand where your code is going wrong:

  • Print Statements: Add print statements to see intermediate values.
  • Online Debuggers: Use platforms like Visualgo.net to visualize data structures and algorithms.
  • Trace Edge Cases: Manually simulate your code for edge cases and identify errors.

Build a Strong Foundation

Sometimes, getting stuck on hard problems indicates a gap in foundational knowledge. Focus on:

  • Data Structures: Arrays, linked lists, stacks, queues, heaps, trees, and graphs.
  • Algorithms: Sorting, searching, recursion, and dynamic programming.
  • Math Basics: Modular arithmetic, combinatorics, and prime factorization.

Stay Persistent and Track Progress

Hard problems can take hours, even days, to solve. Persistence is key:

  • Set Small Goals: Aim to solve one hard problem per week initially.
  • Track Your Progress: Maintain a journal of problems solved, key learnings, and areas for improvement.
  • Celebrate Milestones: Completing a hard problem is an achievement worth celebrating.

Practice Consistently

Consistency is more important than intensity. Regular practice helps you:

  • Retain concepts better.
  • Recognize problem patterns faster.
  • Build the confidence to tackle harder problems.

Conclusion: Don’t Fear the Hard Problems

LeetCode’s hard problems aren’t meant to discourage you—they’re designed to stretch your skills and make you a better programmer. By approaching these problems methodically and focusing on continuous improvement, you’ll gradually master them.

If you’re stuck, don’t hesitate to take a break, revisit the problem later, or seek help from the LeetCode community. Every hard problem you solve will take you one step closer to becoming a coding expert.

Let me know in the comments how you approach hard problems or share your favorite tips!

  • How to solve LeetCode hard problems
  • Tips for solving coding problems
  • LeetCode hard questions strategies
  • Mastering hard problems on LeetCode
  • Problem-solving tips for coding interviews

Popular posts from this blog

Maximum Difference Between Even and Odd Frequency | LeetCode

We are given a string consisting of lowercase English letters. Our task is to find the maximum difference between the frequency of two characters in the string such that: One of the characters has an even frequency . The other character has an odd frequency . The difference is calculated as:  odd_frequency - even_frequency We need to return the maximum possible difference between the odd and even frequencies. Example Walkthrough Let's take a couple of examples to better understand the problem: Example 1: Input:  s = "aaaaabbc" Frequencies: 'a' → 5 (odd) 'b' → 2 (even) 'c' → 1 (odd) Here, the maximum odd frequency is 5 (for 'a') and the maximum even frequency is 2 (for 'b'). Therefore, the result is: maxOdd - maxEven = 5 - 2 = 3 Example 2: Input:  s = "abcabcab" Frequencies: 'a' → 3 (odd) 'b' → 2 (even) 'c' → 2 (even) The maximum odd frequency is 3 (for 'a') and the maximum even fr...

Top 10 Beginner-Friendly LeetCode Questions and Their Solutions

If you're new to solving coding problems on LeetCode, it can feel overwhelming. Where do you start? Which problems are suitable for beginners? Don’t worry! In this blog post, I’ll guide you through   10 beginner-friendly LeetCode questions   that are perfect for getting started on your coding journey. These problems will help you build confidence, improve your problem-solving skills, and lay a solid foundation in data structures and algorithms. Why Start with Beginner-Friendly Problems? Before diving into advanced topics like dynamic programming or graph theory, it’s essential to: Build a strong foundation in basic programming concepts. Understand how to approach a coding problem methodically. Gain familiarity with LeetCode’s platform and its problem structure. The following problems are simple yet impactful, designed to introduce you to common techniques like loops, arrays, strings, and basic math operations. 10 Beginner-Friendly LeetCode Problems 1.  Two Sum (Easy) Prob...

Maximize Amount After Two Days of Conversions | Leetcode Question

When tackling the problem of maximizing the amount of currency after two days of conversions, we encounter an interesting graph-based problem that involves working with exchange rates between various currencies. In this article, we will explore this problem in detail, starting with the brute force approach and refining it to an optimized solution. Problem Explanation You are given a string initialCurrency (the starting currency), along with four arrays: pairs1 and rates1 : Represent exchange rates between currency pairs on Day 1. pairs2 and rates2 : Represent exchange rates between currency pairs on Day 2. The task is to maximize the amount of initialCurrency you can have after performing any number of conversions on both days. You can make conversions using Day 1 rates and then further conversions using Day 2 rates. Key Insights: Conversion rates are valid (no contradictions). Each currency can be converted back to its counterpart at a reciprocal rate (e.g., if USD -> EUR = 2....