2 Big O notation
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In this chapter, we will unlock the power of Big O notation, a fundamental tool for analyzing the efficiency of algorithms in terms of both time complexity (how runtime scales with input size) and space complexity (how memory usage scales). We will explore common time complexities like O(1), O(log n), O(n), and others, along with their real-world implications for choosing the right algorithms and optimizing code. Understanding Big O notation is not only essential for writing scalable and performant software but also for acing technical interviews, as it demonstrates your ability to think critically about algorithmic efficiency. In this chapter we will cover:
- Big O time complexities
- Space complexity...
