# **Evaluating Computational Solutions** This document outlines the key criteria used to evaluate the quality and performance of algorithms and computational solutions, including correctness, clarity, and efficiency, with illustrative examples and case studies. ## **2.2.1 Correctness** An algorithm is considered *correct* if it produces the expected, accurate output for all possible valid inputs. Correctness is verified through comprehensive testing that includes a wide range of scenarios and *edge cases* (e.g., empty inputs, single-item inputs, very large inputs). - **Example 1: Bubble Sort Algorithm** - **Test Case:** Given an unsorted array `[5,2,9,1,5,6]`. - **Expected Output:** A correctly sorted array `[1,2,5,5,6,9]`. - **Verification:** The algorithm is deemed correct if it consistently sorts various arrays, including empty ones or those with only one element. - **Example 2: Binary Search Algorithm** - **Test Case:** Given a sorted array `[1,2,3,4,5]` and a search key of `3`. - **Expected Output:** The index `2` (assuming zero-based indexing). - **Verification:** Correctness is confirmed by testing with different keys (both existing and non-existing) and arrays of varying sizes. ## **2.2.2 Clarity** An algorithm is *clear* if its logic, steps, and structure are easy to read and understand. Clarity is crucial for maintenance, debugging, and collaboration. - **Methods to Enhance Clarity:** - Use descriptive and meaningful names for variables and functions. - Structure the code with logical, sequential steps. - Include comments and documentation to explain complex parts of the code. - **Example: Factorial Function Implementations** * **Case 1: Clear Code** This implementation uses a descriptive function name (`factorial`) and a standard `if` statement, making the logic immediately understandable. ```Python def factorial(n): if n == 0: return 1 else: return n * factorial(n - 1) ``` * **Case 2: Less Clear Code** This version uses a short, non-descriptive function name (`f`) and a *ternary operator*. While more concise, it can be less readable for developers unfamiliar with the syntax or the codebase. ```Python def fct(n): return 1 if n == 0 else n * fct(n - 1) ``` ## **2.2.3 Efficiency** *Efficiency* measures how well an algorithm utilizes computational resources, primarily **time** and **memory (space)**, in relation to the size of the input. It is commonly expressed using *Big O notation*. ### **Time Complexity** *Time complexity* estimates the amount of time an algorithm takes to run as a function of the size of its input (*n*). It focuses on the growth rate of execution time, not the exact time in seconds. ### **Space Complexity** *Space complexity* measures the amount of memory (or storage space) an algorithm uses as a function of the size of its input. This includes both the space for the input and any auxiliary space used during execution. ### **Big O Notation** *Big O notation* is a mathematical notation used to describe the limiting behavior of a function when the argument tends towards a particular value or infinity. In computer science, it describes the *worst-case scenario* for an algorithm's performance, providing an upper bound on its time or space usage. | Big O Notation | Name | Description | | ------------------------------------------------ | --------------- | ------------------------------------------------------------------------------------------------------- | | **O(1)** | Constant | Execution time is constant and does not change with the input size. | | **O(log n)** | Logarithmic | Execution time grows logarithmically; the algorithm becomes slightly slower as input size increases. | | **O(n)** | Linear | Execution time grows linearly with the input size. Doubling the input size roughly doubles the run time. | | **O(n log n)** | Linearithmic | A combination of linear and logarithmic growth. Common in efficient sorting algorithms like Merge Sort. | | **O(n²)** | Quadratic | Execution time is proportional to the square of the input size. Often involves nested loops. | ### **Algorithm Efficiency Examples** #### **Linear Search Algorithm** A sequential search that checks each element in a list one by one until the target value is found or the list ends. - **Algorithm Steps:** 1. Start at the first element of the array. 2. Compare the target value with the current element. 3. If they match, the search is successful. 4. If not, move to the next element and repeat until a match is found or the end of the array is reached. - **Time Complexity:** **O(n)**. In the worst case, the algorithm must check every element in the array. - **Space Complexity:** **O(1)**. It uses a fixed amount of extra memory regardless of the input size (e.g., for an index variable). #### **Binary Search Algorithm** An efficient search algorithm that finds a target value within a *sorted* array by repeatedly dividing the search interval in half. - **Algorithm Steps:** 1. Compare the target value with the middle element of the sorted array. 2. If they match, the search is successful. 3. If the target is less than the middle element, repeat the search on the left half of the array. 4. If the target is greater than the middle element, repeat the search on the right half of the array. 5. Continue this process until the value is found or the interval is empty. - **Time Complexity:** **O(log n)**. With each step, the search space is halved, drastically reducing the number of comparisons needed for large datasets. - **Space Complexity:** **O(log n)** for a *recursive implementation*, due to the call stack depth. An *iterative implementation* can achieve **O(1)** space complexity. ## **2.2.4 Case Studies** #### **Problem 1: Find Greatest Common Divisor (GCD)** - **Problem Statement:** Given two integers, find their greatest common divisor. - **Solution:** *Euclidean Algorithm* 1. Take two integers, `a` and `b`. 2. While `b` is not zero, update `a` to `b` and `b` to the remainder of `a / b`. 3. When `b` becomes zero, `a` holds the GCD. - **Evaluation:** - **Correctness:** Correctly finds the GCD based on mathematical principles. - **Clarity:** The iterative process is simple, elegant, and easy to follow. - **Efficiency:** - *Time Complexity:* **O(log min(a, b))** - *Space Complexity:* **O(1)** #### **Problem 2: Count Number of Vowels in a String** - **Problem Statement:** Given a string, count the total number of vowels ('a', 'e', 'i', 'o', 'u'). - **Solution:** *Simple Iteration* 1. Initialize a counter to 0. 2. Loop through each character of the string. 3. If the character is a vowel, increment the counter. 4. Return the final count. - **Evaluation:** - **Correctness:** Accurately counts vowels by checking every character. - **Clarity:** The logic is straightforward and very easy to understand. - **Efficiency:** - *Time Complexity:* **O(n)**, where *n* is the length of the string. - *Space Complexity:* **O(1)** ## **Additional Evaluation Metrics** Beyond correctness and efficiency, modern systems must be evaluated for scalability and robustness. When deploying solutions, developers often face trade-offs between making a system easy to use and keeping it secure. ## **Possible Questions/Answers** - **Q:** What is the difference between Time and Space Complexity? **A:** *Time Complexity* measures how the runtime of an algorithm scales with the input size, while *Space Complexity* measures how the memory usage of an algorithm scales with the input size. - **Q:** What does Big O notation represent in algorithm analysis? **A:** Big O notation describes the upper bound or *worst-case scenario* of an algorithm's performance, providing a way to classify its efficiency as the input size grows. - **Q:** Why is Binary Search generally more efficient than Linear Search? **A:** Binary Search is more efficient for *sorted* arrays because it eliminates half of the remaining data at each step (O(log n)), whereas Linear Search must check every element in the worst case (O(n)). - **Q:** What is A/B testing in the context of usability? **A:** It is a method of comparing two versions of a webpage or app against each other to determine which one performs better. ## **Comparison of Algorithm Complexities** | Algorithm | Time Complexity | Space Complexity (Iterative) | | ------------------------- | -------------------- | ---------------------------- | | Linear Search | O(n) | O(1) | | Binary Search | O(log n) | O(1) | | Euclidean Algorithm (GCD) | O(log min(a, b)) | O(1) |

2.2 Evaluating Computational Solutions | Computational Thinking & Algorithms | Computer 12