Algorithmic Design and Data Structure Techniques
Applying Algorithmic Design and Data Structure Techniques When creating applications, there are complexities of time and space that must both be considered. Simply speaking, time complexity is the amount of time a computer algorithm takes to run. This time is measured in milliseconds, but when applied to a larger application with a large amount of data, this time becomes much more important. Space complexity considers how much space or memory an algorithm uses to solve a problem. As the algorithm uses more data, space may be freed up with each computer cycle or keep growing. If the space necessary continues to grow, there is a possibility of running out of memory. Using algebraic calculations, more commonly known as “Big O” notation, a software designer can determine if time grows by a number equal to the input or if there is an exponential growth in time as input increases. “Big O” notation can also be used to determine if memory usage remains constant or continues to grow...