Using algorithmic design and data-structure techniques in structured programming 101

 

As I begin this blog, I realize I’m still a newcomer to algorithms and data structures. Every computer depends on these fundamentals, and understanding them helps you determine what a system needs. Before you start designing, you must choose an appropriate data structure and a suitable algorithm.

Data structures are ways to organize and store information in a computer so operations can be done efficiently. Common examples include:

• Arrays: collections of same-type items stored in consecutive memory locations.
• Linked lists: sequences of items connected in a linear chain.
• Stacks: last-in, first-out (LIFO) structures where the most recently added item is removed first.

An algorithm is a defined procedure or set of rules for a computer to perform calculations or solve problems. Frequent algorithm design strategies include divide and conquer, approached from both top-down and bottom-up perspectives. Top-down design decomposes a problem into smaller modules. Bottom-up minimizes recursion by building and verifying components individually.

Most people worldwide have used Google, which holds roughly 86% of the search market. Google’s ranking of web pages relies on sophisticated algorithms that assess relevance. In doing so, Google employs well-known searching and sorting algorithms grounded in data-structure theory.

Incorporating algorithmic design and data-structure techniques leads to structured programs that are easier to maintain and run efficiently. Benefits to programmers include code that’s more readable and user-friendly, with development taking less time and effort.