Completing Algorithms

Understanding Algorithms

An algorithm is a finite, well-defined procedure to solve a problem or perform a specific task.
Algorithms are important in computer science as they define a series of steps for a computer to follow in order to solve a problem or complete a task.
Algorithms can be expressed in many forms, including natural language, pseudocode, flowcharts, and programming languages.

Completing Algorithms

Completion of an algorithm involves determining the steps needed to solve a problem, and then implementing those steps in a structured format.
The first step is problem understanding and decomposition. Analyze the problem and break it down into manageable parts or sub-problems. This technique is known as decomposition.
Each sub-problem is then solved individually and the solutions are combined to solve the original problem - this is a technique called divided and conquer.

Designing Algorithms

When designing an algorithm, think about the inputs needed and the outputs expected.
Also consider any data structures (like arrays or lists) that might be used to hold data, or any variables that might need to be defined.
One of the most crucial considerations is the logic of the algorithm – the sequence of operations (addition, subtraction, etc.), decision-making (if, else) and repetition (loops) that will ultimately solve the problem.
Always remember, a well-designed algorithm is efficient and doesn’t take unnecessary steps or process unnecessary data.

Ensuring Algorithms are Correct

The correctness of an algorithm is extremely important - a correct algorithm accurately solves the problem it’s designed for.
To ensure an algorithm’s correctness, use techniques such as dry running, which involves tracing through an algorithm step by step to make sure it works as expected.
Frequently test your algorithms with a variety of inputs (including edge cases) to ensure they produce the correct outputs and handle all scenarios.
Remember, an incorrect algorithm can cause serious problems, like incorrect results or infinite loops.

Assessing Algorithm Efficiency

The efficiency of an algorithm refers to the amount of computational resources, namely time and space, it uses.
Measure an algorithm’s time efficiency by considering its time complexity - how the time to complete the algorithm increases as the size of the input increases.
Consider space efficiency by determining the algorithm’s space complexity - how much memory is required to run the algorithm.
Always seek to optimise your algorithms to make them as efficient as possible.

Adapting Algorithms into Code

Once an algorithm is completed, it becomes the blueprint to indicate the code development in a specific programming language.
Be wary that different languages may require different constructs or syntax, but the algorithm dictates the core logic.
Remember: the algorithm outlines the solution, and the code implements it.