Problem Solving Process

When using a computer as a model for problem-solving, we can think of the process in terms of how we interact with and instruct the computer to solve a given problem. Here’s how the problem-solving process can be explained in six steps, aligning with how computers handle tasks

1. Problem Definition: The first step is to clearly define the problem. This includes understanding the requirements, constraints, and the desired output. The problem should be broken down into smaller, manageable components.
2. Algorithm Design: Once the problem is understood, the next step is to design an algorithm—a step-by-step procedure or set of rules to solve the problem. The algorithm should be efficient, both in terms of time and space complexity, and should be validated for correctness.
3. Implementation: After designing the algorithm, it is translated into a computer program using a programming language. The program is composed of instructions that the computer can execute to perform the desired task. This involves coding, debugging, and testing. 
4. Execution:The computer executes the program, following the instructions sequentially or as directed by control structures (loops, conditionals, etc.). The computer, as a model of computation, processes the inputs and produces outputs according to the logic defined in the program.
5. Testing and Verification(debugging): The program is tested with different inputs to ensure it behaves as expected. Testing helps in identifying any errors or inefficiencies in the implementation. Verification ensures that the program meets the specifications defined in the problem statement.Debug any errors encountered, whether they are logical, syntactical, or runtime errors.
6. Optimization: If necessary, the program is optimized for better performance, which might involve refining the algorithm or improving the code to reduce runtime or memory usage.
7. Documentation and Maintenance: Finally, the process involves documenting the program, which includes explaining the code, algorithms, and design decisions. This documentation is crucial for future maintenance, updates, and debugging.

Throughout this process, the computer acts as a model of computation, executing algorithms and processing data as per the designed instructions to solve the problem effectively.

Example:

Problem Statement

Let's say we have a simple problem: Find the sum of all even numbers between 1 and 100.

Step 1: Problem Definition

First, let's clearly define the problem:

• Input: The range of numbers from 1 to 100.
• Output: The sum of all even numbers within this range.

Our task is to identify all the even numbers between 1 and 100 and then add them together to get the total sum.

Step 2: Algorithm Design

Now, we need to design an algorithm to solve this problem. Here's a step-by-step approach:

1. Initialize a variable sum to 0. This will store the total sum of even numbers.
2. Loop through all numbers from 1 to 100.
3. Check if the current number is even. If it is, add it to sum.
4. Return or print the value of sum after the loop ends.

This algorithm is simple and efficient for our problem.

Step 3: Implementation

Next, we'll implement this algorithm in Python. Here’s how the code would look:

sum = 0
for number in range(1, 101):
    if number % 2 == 0:
        sum += number
print("The sum of all even numbers between 1 and 100 is:", sum)

Step 4: Execution

When we run this code, the computer follows the instructions we've provided:

• It initializes sum to 0.
• It loops through each number from 1 to 100.
• For each number, it checks if the number is even (i.e., number % 2 == 0).
• If the number is even, it adds it to the sum variable.
• After the loop finishes, it prints the final value of sum.

Step 5: Testing and Verification

Let’s test the code to see if it works correctly. When we run the code, the output should be:

The sum of all even numbers between 1 and 100 is: 2550We can verify this by manually calculating or using a mathematical formula. The code has worked correctly as expected.

Step 6: Optimization (if needed)

In this case, the solution is already optimal for the problem size. However, if we were dealing with a much larger range or needed to run this operation frequently, we might look for ways to optimize, such as using a mathematical formula to calculate the sum of even numbers directly:

$\text{Sum}=2+4+6+\cdots +100$

Sum=2+4+6+⋯+100

This can be represented as:

$\text{Sum}=2\times (1+2+3+\cdots +50)$

Sum=2×(1+2+3+⋯+50)

The sum of the first 50 natural numbers is given by:

$\text{Sum}=\frac{n\times (n+1)}{2}$

Sum=2n×(n+1)​

So, the sum becomes:

$\text{Sum}=2\times \frac{50\times 51}{2}=2550$

Sum=2×250×51​=2550

Step 7: Documentation and Maintenance

Finally, it's good practice to add comments and documentation to your code so that others (or you in the future) can easily understand what the code does.

# This program calculates the sum of all even numbers between 1 and 100.
# Initialize the sum variable

sum = 0

# Loop through numbers from 1 to 100

for number in range(1, 101):
# Check if the number is even
    if number % 2 == 0:
# Add the even number to the sum
    sum += number
# Output the result
print("The sum of all even numbers between 1 and 100 is:", sum)

Conclusion

In this example, we’ve followed the problem-solving process with a computer as a model of computation:

1. Defined the problem.
2. Designed an algorithm.
3. Implemented the algorithm in a programming language.
4. Executed the program.
5. Tested and verified the output.
6. Considered optimization.
7. Documented the code.

This systematic approach helps in solving any computational problem efficiently.