An algorithm or algo is a step-by-step technique that specifies a series of instructions that must be followed in a precise order to achieve the desired outcome. Algorithms are usually designed independently of the underlying programming language, which means that they can be implemented in any programming language.
An algorithm’s properties include ambiguity, fineness, efficacy, and language independence. An algo’s scalability and performance are the major elements that influence its importance.
- What is an Algorithm? [Definition]
What is an Algorithm? [Definition]
An algorithm is a collection of instructions that a computer must follow in order to execute calculations or other problem-solving tasks. It is a finite set of instructions carried out in a certain order to execute a task, according to its formal specification.
Please note that an algo is not the whole program or code; rather, it is simple reasoning for a problem expressed in the form of a flowchart or pseudocode. Following are some important terms associated with an algorithm:
- Problem: An issue is a real-world problem for which you must create a program or set of instructions. A set of instructions is referred to as an algo.
- Algorithm: An algo is a step-by-step procedure that is created to solve a problem.
- Input: The algorithm is given the necessary and desired inputs after it has been designed.
- Processing unit: The input will be sent to the processing unit, which will produce the desired output.
- Output: The output of a program is the outcome or result of the program.
Characteristics of an Algorithm
An algo necessitates some input values. A value other than 0 can be used as input to an algo. You will have one or more results at the end of an algo. The following are the features of an algorithm:
- Effectiveness: Because each instruction in an algo has an impact on the whole process, it must be effective.
- Finiteness: An algorithm must have a finite number of steps. In this case, finiteness implies that the method has a finite number of instructions, i.e., the instructions are countable.
- Language independence: A language-independent algo is one whose instructions can be written in any language and yield the same results.
- Unambiguity: An ideal algo is characterized as one with clear and straightforward instructions.
What is the Purpose of an Algorithm?
Algos are required for the following reasons:
It will help you grasp the concept of scalability. When faced with a large real-world problem, it is necessary to break it down into little pieces in order to examine it rapidly.
Breaking down the real-world problem into smaller units is difficult. It implies that a problem is possible if it can be easily split into smaller steps. After you’ve figured out what an algorithm is and why you need one, you’ll look at an example of how to write one.
What is the Best Way to Write an Algorithm?
Algorithm writing does not have any well-defined standards. It is, however, a resource-constrained task. Algos are never written in a programming language.
Basic code constructs such as loops like do, for, and while are shared by all programming languages, as are flow control elements like if-else. These standard constructs can be used to write an algo.
Algorithms are usually written in sequential order, but this isn’t always the case. After the problem area has been well-defined, algo writing takes place. That is, you must be aware of the problem area for which a solution is being developed.
Important Factors to Keep in Mind While Writing an Algorithm
When creating an algorithm, keep the following factors in mind:
- Modularity: If you take an issue and break it down into smaller modules or steps, which is a basic description of an algo, this feature was made for it.
- Correctness: The correctness of an algorithm is defined as when the given inputs yield the expected result, suggesting that the algo was successfully developed. The analysis of an algo has been finished correctly.
- Maintainability: It refers to the fact that the algo should be written in a simple, structured manner so that when it is redefined, no significant modifications are made.
- Functionality: It considers a number of logical steps in order to solve a real-world situation.
- Robustness relates to an algorithm’s capacity to properly characterize your problem.
- User-friendly: The designer will not communicate the algo to the programmer if it is difficult to understand.
- Simplicity: An algo that is easy to grasp is simple.
- Extensibility: If another algorithm designer or programmer wants to utilize your algo, it should be extensible.
Types of Algorithms
1) Search Algorithm
You look for something in your daily life every day. Similarly, a computer stores a huge quantity of data, and whenever a user asks for information, the computer searches the memory for that information and returns it to the user. For searching data in an array, there are essentially two methods:
- Linear Search
Linear search is a basic algo that starts looking for an element or a value at the beginning of an array and keeps looking until the needed element is found. It compares the element to be searched with all of the items in an array and returns the element index if a match is found; otherwise, it returns -1. This algorithm can be used on a list that isn’t sorted.
- Binary Search
The most basic algo is a binary algorithm, which searches for elements quickly. It’s used to locate a specific element in a sorted list. The items must be stored in sequential order or sorted in order to apply the binary method. Binary search is impossible to implement if the elements are stored at random.
2) Sort Algorithm
Sorting algorithms assign ascending or descending order to elements in an array or a data structure. The element’s new order is determined by the comparison operator.
Complexity of Algorithms
The performance of the algo can be assessed in two ways:
1. Time Complexity
The time complexity of an algorithm refers to the amount of time it takes to complete its execution. The temporal complexity of an algo is expressed using the Big O notation. In this case, the asymptotic notation for quantifying temporal complexity is Big O notation. The number of steps necessary to finish the execution is used to determine the temporal complexity.
2. Space Complexity
The space complexity of an algorithm refers to how much space it takes to solve a problem and produce an output. Big O notation is used to indicate both space and time complexity. Measuring the space complexity is necessary for an algo for the following reasons:
- To keep a track of program instructions.
- To keep a record of constant values.
- For keeping a track of the values of variables.
- To keep a track of function calls, jump statements, and so on.
So, an algorithm is a way through which a computer converts input data into output data. Although it is a simple concept, every piece of technology you come into contact with contains numerous algos.
Perhaps the next time you pick up your phone, watch a Hollywood film, or check your email, you can appreciate the kind of complicated algorithms at work.