#### Overview of this book

Begin your journey into the fascinating world of algorithms with this comprehensive course. Starting with an introduction to the basics, you will learn about pseudocode and flowcharts, the fundamental tools for representing algorithms. As you progress, you'll delve into the efficiency of algorithms, understanding how to evaluate and optimize them for better performance. The course will also cover various basic algorithm types, providing a solid foundation for further exploration. You will explore specific categories of algorithms, including search and sort algorithms, which are crucial for managing and retrieving data efficiently. You will also learn about graph algorithms, which are essential for solving problems related to networks and relationships. Additionally, the course will introduce you to the data structures commonly used in algorithms. Towards the end, the focus shifts to algorithm design techniques and their real-world applications. You will discover various strategies for creating efficient and effective algorithms and see how these techniques are applied in real-world scenarios. By the end of the course, you will have a thorough understanding of algorithmic principles and be equipped with the skills to apply them in your technical career.
Free Chapter
Chapter 1: Introduction to Algorithms
Chapter 2: Pseudocode and Flowcharts
Chapter 3: Algorithm Efficiency
Chapter 4: Basic Algorithm Types
Chapter 5: Search Algorithms
Chapter 6: Sort Algorithms
Chapter 7: Graph Algorithms
Chapter 8: Data Structures Used in Algorithms
Chapter 9: Algorithm Design Techniques
Chapter 10: Real World Applications of Algorithms
Conclusion
Where to continue?

# 3.4 Practice Problems

Let's consolidate our learning with some practice problems. These problems are designed to help you think about how algorithms work and to practice analyzing their efficiency in terms of time and space complexity. For each problem, try to write the pseudocode, determine the time and space complexity, and explain your reasoning.

## Linear Search:

• Implement a function linear_search(list, item) that takes a list and an item, and returns the index of the item if it is found in the list, and -1 otherwise.
• Example input: linear_search([1, 3, 5, 7, 9], 5)
• Example output: 2
• Think about: What is the time and space complexity of your implementation?

Solution:

This problem can be solved by iterating through the given list and checking if each item is equal to the target item.

Time Complexity: O(n) - In the worst-case scenario, we need to look at each item in the list.

Space Complexity: O(1) - We are not creating any new data structures that grow with the size...