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Algorithms
In computer science, an algorithm is an unambiguous specification of how to solve a class of problems. Algorithms can perform calculations, data processing and automated reasoning tasks.
An algorithm is an effective method that can be expressed within a finite amount of space and time and in a well-defined formal language for calculating a function. Starting from an initial state and initial input (perhaps empty), the instructions describe a computation that, when executed, proceeds through a finite number of well-defined successive states, eventually producing "output" and terminating at a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate random input.
There are certain requirements that an algorithm must abide by:
- Definiteness: Each step in the process is precisely stated.
- Effective Computability: Each step in the process can be carried out by a computer.
- Finiteness: The program will eventually successfully terminate.
Some common types of algorithms include sorting algorithms, search algorithms, and compression algorithms. Classes of algorithms include Graph, Dynamic Programming, Sorting, Searching, Strings, Math, Computational Geometry, Optimization, and Miscellaneous. Although technically not a class of algorithms, Data Structures are often grouped with them.
Efficiency
Algorithms are most commonly judged by their efficiency and the amount of computing resources they require to complete their task. A common way to evaluate an algorithm is to look at its time complexity. This shows how the running time of the algorithm grows as the input size grows. Since the algorithms today, have to operate on large data inputs, it is essential for our algorithms to have a reasonably fast running time.
Big O
To describe the running time and efficiency of algorithms, the standard language and metric used is called Big O notation and can be denoted as O(running time) for example the time taken to search an element using linear search (i.e to find an element by comparing it to all the other elements in an array of size n) is O(n). The notation helps us to understand how fast a particular algorithm is by making sure that it is at least as fast as the denoted run-time. Big_O - wikipedia
Sorting Algorithms
Sorting algorithms come in various flavors depending on your necessity. Some, very common and widely used are:
Quick Sort
There is no sorting discussion which can finish without quick sort. Quick Sort
Merge Sort
It is the sorting algorithm which relies on the concept how to sorted arrays are merged to give one sorted arrays. Read more about it here- Merge Sort
Heap Sort
A sorting algorithm that works by first organizing the data to be sorted into a special type of binary tree called a heap. The heap itself has, by definition, the largest value at the top of the tree, so the heap sort algorithm must also reverse the order. Read more about it here- Heap Sort
freeCodeCamp's curriculum heavily emphasizes creating algorithms. This is because learning algorithms is a good way to practice programming skills. Interviewers most commonly test candidates on algorithms during developer job interviews.
Further Resources
Intro to Algorithms | Crash Course: Computer Science
This video gives an accessible and lively introduction to algorithms focusing on sorting and graph search algorithms.
What is an Algorithm and Why Should you Care? | Khan Academy
This video introduces algorithms and briefly discusses some high profile uses of them.
15 Sorting Algorithms in 6 Minutes | Timo Bingmann
This video visually demonstrates some popular sorting algorithms that are commonly taught in programming and Computer Science courses.
How algorithms shape our world | Kevin Slavin
This is a short video on how algorithms shape our world and their occurence in everyday life.
This is also a really good open source project that helps you visualize algorithms.
Infographic on how Machine Learning Algorithms Work
This infographic shows you how unsupervised and supervised machine learning algorithms work.
An entire course on Khanacademy on algorithms
This is a good step by step introduction to algorithms.