If an algorithm's resource consumption, often referred to as computational cost, is at or below a certain threshold, it is said to be efficient. Generally speaking, "acceptable" indicates that it will operate on a machine that is available in a fair amount of time or space, usually based on the size of the input.
<h3>Explain about the efficiency of an algorithm?</h3>
Growth requires an understanding of an algorithm's effectiveness. Programmers write code with the future in mind, and efficiency is essential to achieve this. Reducing the number of iterations required to finish your task in relation to the size of the dataset is the goal of efficient algorithm development.
The use of asymptotic analysis can frequently help to solve these issues. As the size of the input increases, asymptotic analysis quantifies an algorithm's effectiveness or the program that implements it.
To express how time-consuming a function is, we use a method called "Big O notation." We use the Big O notation, a language, to describe how time-consuming an algorithm is. It's how we assess the value of several approaches to an issue, and U supports our decision-making.
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