1. **Problem Statement:** Understand the concepts of growth of functions and complexity in algorithm analysis. 2. **Growth of Functions:** This refers to how the running time or space requirements of an algorithm increase as the input size increases. 3. **Common Growth Rates:** Examples include constant $O(1)$, logarithmic $O(\log n)$, linear $O(n)$, quadratic $O(n^2)$, and exponential $O(2^n)$. 4. **Big-O Notation:** Used to describe the upper bound of an algorithm's growth rate, focusing on the dominant term and ignoring constants. 5. **Complexity:** Measures the resources (time or space) an algorithm uses relative to input size. 6. **Time Complexity:** How the execution time increases with input size. 7. **Space Complexity:** How the memory usage increases with input size. 8. **Example:** For a loop running $n$ times, time complexity is $O(n)$. 9. **Summary:** Growth of functions helps classify algorithms by efficiency, and complexity quantifies resource usage.