Algorithmic design and data structure techniques are essential in developing structured programs. Algorithmic design is the process of creating a step-by-step procedure to solve a problem. Data structure techniques are used to organize and store data to make it easy to access and manipulate.
When applying algorithmic design and data structure techniques in developing structured programs, it is important to understand the problem before you start designing an algorithm, including identifying any inputs, outputs, constraints, and requirements of the problem. Several algorithmic design techniques exist, such as divide and conquer, dynamic programming, and greedy algorithms. Choose the most appropriate for the problem you’re trying to solve. Once you’ve chosen an algorithmic design technique, you must select a right data structure to store and manipulate the data. This could be an array, linked list, stack, queue, tree, or graph. Once you’ve chosen an algorithmic design technique and a data structure, it’s time to implement the algorithm and then test it thoroughly to ensure it works correctly.
Some algorithms and data structure designs are better than others. The performance of an algorithm or data structure is usually measured in terms of its time complexity and space complexity. Time complexity is the amount of time an algorithm takes to complete its task. Space complexity is the amount of memory an algorithm requires to execute. For example, a binary search algorithm takes a list and divides it into two, looks at the middle entry, then further breaks the list in half again and repeats this process until it finds the item it is looking for. Whereas a sequential search checks every element until it finds the desired element.
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