Applying Algorithmic Design and Data Structure Techniques

 

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                Programs must be efficient, maintainable, and scalable when developing a structured program. Applying the right algorithmic design and data structure techniques can ensure your program meets or exceeds these standards while providing developers with the skills and experience needed to create well-structured programs. The first step in the process is understanding the problem and any requirements or restrictions. To better understand the problem you are trying to solve, it is a good practice to break it into smaller parts, making it more manageable and less complex. Once we have figured out the problem and its requirements, selecting the appropriate data structures will be less challenging.

                To represent and organize your data effectively and efficiently, we must pick a data structure that closely matches our problem’s requirements and constraints. The factors that can help inform your decision could include the type of data, operations to be performed, and how the data will be accessed. Choosing the appropriate data structure can help provide an optimized environment for specific functions like inserting and deleting data. At the same time, the proper data structure can help keep your data organized, improving accuracy and efficiency. According to Shaffer (2013), “Only after carefully analyzing your problem’s characteristics can you determine the best data structure for the task” (p. 6). It is also a good idea to weigh the cost and benefits while also considering the time and space constraints of the problem.

                Once we have the data structure figured out, we can design an algorithm to perform operations on our data. We will need to consider the steps required to achieve the desired outcome while also considering the best approach. It is crucial to consider the time complexity or the execution time relative to the input size, which can impact how long an algorithm may run. Low time complexity is preferred and most often described in Big O notation. For example, the time complexity of O(1) would be much faster than that of O(n) because it does not depend on the input size. In addition, space complexity, or how much memory is needed to solve a problem, is also an essential aspect of design. An algorithm with a low space complexity will use less memory, which can be important when working with limited resources.

                Other vital factors for algorithm and data structure design include scalability and correctness. Your algorithm and data structure should be scalable or handle varying input sizes without sacrificing performance. More importantly, the algorithm or data structure should always produce correct and expected results. However, some algorithms and data structures are better than others, but this often depends on the problem and goals we are trying to solve or achieve. For example, when searching for a specific item in a list, one could use a linear search algorithm with a linked list data structure or a binary search with an array. Although the binary search time complexity is lower, it adds more operations like sorting the list, but the linear search may be more suited for unsorted and frequently modified elements. Depending on the size of the data, if it needs to be sorted, and how often it will be altered or accessed will ultimately determine which algorithm and data structure is best.

 

Reference

Shaffer, C. A. (2013). Data Structures and Algorithm Analysis (Edition 3.2). Retrieved from http://people.cs.vt.edu/~shaffer/Book/JAVA3elatest.pdf