**Motivation**

What makes computers useful for us is primarily the ability to solve problems. The procedure in which computers solve a problem is an **algorithm**. In the recent context of increasing number of algorithms available for solving data-related problems, there is increasing demand for higher level of understanding of algorithm’s performance in order for data scientists to choose the right algorithms for their problems.

Having a general perception for efficiency of an algorithm would help shaping the thought process for creating or choosing better algorithms. With this intention in mind, I would like to create a series of posts to discuss about what makes a good algorithm in practice, or in short, **efficient algorithm**. And this article is the first step of the journey.

**Define Efficiency**

An algorithm is considered efficient if its resource consumption, also known as computational cost, is at or below some acceptable level. Roughly speaking, ‘acceptable’ means: it will run in a reasonable amount of time or space on an available computer, typically as a function of the size of the input.

There are many ways in which the resources used by an algorithm can be measured: the two most common measures are **speed** and **memory usage**. In the next 2 sections, we will be looking at the two different perspectives for measuring efficiency of an algorithm from theoretician and practitioners.

**Theoreticians perspective**

**asymptotic runtimes**. Or in other words, they look at how the runtime scale with input size (n) as n gets larger. Does the output scale proportional to n, or proportional to n squared, or maybe exponential in n? In fact, these

**rate of growth**are so different that as long as n is sufficiently large, constant multiples that come from other measures like temporary disk usage, long-term disk usage would be relatively small and neglected.

**Practitioner perspective**

In practice, there are other factors which can affect the efficiency of an algorithm, such as requirements for accuracy and/or reliability. As detailed below, the way in which an algorithm is implemented can also have a significant effect on actual efficiency, though many aspects of this relate to optimization issues.

Implementation issues can also have an effect on efficiency, such as the choice of programming language, or the way in which the algorithm is actually coded, or the choice of a compiler for a particular language, or the compilation options used, or even the operating system being used. In many cases a language implemented by an interpreter may be much slower than a language implemented by a compiler.