Algoritms

Algoritms

Introduction to Algorithms

• Algorithms are well-defined sets of steps used to solve problems or achieve objectives. They are followed to get a specific result.
• The order of operations and the directions provided are fundamental elements of an algorithm.
• In computer science, algorithms are often used to manipulate, process, and analyse data.
• Algorithms are ubiquitous in computing, from simple tasks such as sorting a list of numbers to complex tasks like image recognition.

Types of Algorithms

• There are various types of algorithms such as search algorithms (for finding a particular item in a list), sort algorithms (for ordering a set of items), and computational algorithms (for mathematical computations).
• Other types include collection algorithms (for handling collections of data) and graph algorithms (for analysing network structures).

Characteristics of Good Algorithms

• Accuracy: A good algorithm always provides the correct outputs for given inputs.
• Efficiency: A good algorithm performs tasks as quickly as possible, using the least resources.
• Robustness: A good algorithm can handle unexpected inputs or errors gracefully.
• Maintainability: A good algorithm is designed in such a way it can be easily understood, modified and maintained.

Search Algorithms

• Popular search algorithms include linear search, where every element of the list is checked, and binary search, where the list is halved with each step until the required value is found.
• Considerations when choosing a search algorithm should include: the size of the list, the value to be searched, and the arrangement of the list (sorted or unsorted).

Sort Algorithms

• Common sort algorithms include bubble sort, selection sort, and insertion sort.
• More advanced sort algorithms include quick sort, merge sort, and heap sort.
• Factors to consider when choosing a sort algorithm include: the size of the data set, the level of ‘sortedness’, and the memory resources available.

Designing Algorithms

• Flowcharts and pseudocode are popular tools used to design and visualize an algorithm’s flow and operation before implementation.
• Their use can greatly aid in understanding complex sequences of operations and making sure that all inputs, outputs and processing tasks have been correctly accounted.

Evaluating Algorithms

• Algorithms are evaluated based on two main factors: space complexity (how much memory they use) and time complexity (how long they take to execute).
• The Big O notation is often used to denote the time complexity of an algorithm. It describes worst-case scenarios regarding how the running time of an algorithm can grow relative to the size of the input.