How to master the principles of Leetcode?

Introduction

When it comes to LeetCode, some people memorize all the solutions in a given language, while others starting out are lost on where to begin. Those without traditional algorithmic backgrounds often don't know how to proceed, especially if they've focused on learning by building software solutions. These developers may lack understanding of fundamental theoretical concepts, or the "whys" behind certain approaches—why we implement solutions in specific ways, and how we could do things differently to enhance speed and efficiency.

Why LeetCode Matters

Before starting LeetCode, many question its relevance: "Why do I even need to learn LeetCode? I can code and build programs. I don't need to understand arrays or algorithmic coding—it's not relevant to my day-to-day work."

The short answer? You do need to learn LeetCode to become a better programmer. It helps you:

• Understand how to develop pseudocode
• Think critically about programming
• Grasp the "whys" behind different approaches
• Develop better solutions
• Choose between different runtime options
• Recognize when your program will take 5 days instead of 5 hours
• Select optimal solutions rather than settling for any solution that works

LeetCode helps you choose more efficient solutions, think outside the box, and approach problems pragmatically. It's fundamentally about problem solving.

LeetCode Problem Categories

Now that you're convinced, let's talk about how to approach LeetCode problems. Before tackling individual challenges, understand the general structure of LeetCode problems.

LeetCode problems can be categorized in the following ways:

1. Sliding Window

Typically used in problems related to strings or arrays when you want to optimize for space or time complexity. Examples include:

• Maximum sum subarray of size k
• Longest substring without repeating characters

2. Two Pointers

Important for data points where the order of elements matters. Mostly used in sorted arrays, linked lists, or when dealing with pairs or partitions. Examples include:

• Merging two sorted arrays
• Combining linked lists

In two pointers, you typically look at both the right and left sides of the elements.

3. Fast and Slow Pointers

A variation of two pointers where one pointer moves faster than the other. This technique is often used for problems involving cycles or finding middle elements. Examples include:

• Finding the middle element of a linked list
• Detecting the start of a cycle in a linked list

4. Divide and Conquer

This method is used when you want to solve a bigger problem by dividing it into smaller sub-portions. Each sub-problem is solved recursively, then combined to solve the original problem. The sub-problems can be solved independently and brought together for the final solution. Examples include:

• Merge Sort
• Quick Sort
• Binary Search
• Closest pair of points

5. Backtracking

A method of exploring all possible solutions by building up the answer incrementally and "backtracking" when you hit a dead end. Typically used for problems involving generating combinations, permutations, or paths, such as puzzles or constraint satisfaction problems.

6. Dynamic Programming (DP)

Used when problems have overlapping sub-problems and optimal sub-structures. Solutions are built incrementally, storing intermediate results. Common applications include:

• Fibonacci sequence
• Longest common subsequence

Understanding Big O Notation

The O(n) notation, also referred to as Big O notation, is a way to describe the time and space complexity of an algorithm in terms of the input size (n). It helps quantify an algorithm's performance and how it will scale as the input size increases.

This is crucial because you want to understand the runtime of your algorithm and find the most optimal solution for efficient, faster programs—especially as your applications grow larger.

Big O describes the worst-case scenario of an algorithm's time or space complexity.

Different time complexities include:

• O(1): Constant time. A simple example would be accessing an element in an array by index.
• O(log n): Logarithmic time. Binary search in a sorted array is a common example—the algorithm's runtime grows logarithmically with the input size.
• O(n): Linear time. The algorithm's runtime grows proportionally with the input size.
• O(n log n): Linearithmic time. This is typically the complexity of efficient sorting algorithms like Merge Sort and QuickSort.
• O(n²): Quadratic time. The algorithm's runtime grows quadratically with the input size.

Why Choose the Right Solution?

Understanding different time complexities helps you:

• Identify the right runtime for your problem
• Understand the most optimal and efficient solution
• Evaluate your program's performance
• Identify loopholes in your approach
• Compare different solutions
• Optimize your code

Approach to Learning LeetCode

When starting LeetCode, don't aim to memorize solutions. Instead:

1. Understand your problem thoroughly
2. Learn about Big O notation
3. Read the LeetCode problem carefully
4. Understand the relevant data structures
5. Start by writing pseudocode
6. Justify why you chose a particular solution
7. Only then focus on the specific syntax for your implementation

Learning Timeline

Can you learn LeetCode in one week? Probably not completely. LeetCode requires constant practice, and you shouldn't aim to master it in such a short time. However, you can understand the overall structure and take it from there.

I'm still learning the easy and medium problems myself, but these tips have really helped me make progress.

What's Your Experience?

What has your LeetCode journey been like? What has helped you the most with LeetCode? I'd love to hear your thoughts and experiences in the comments!