What Is An Algorithm? Definition, Types, Characteristics

Use of the Algorithms

Algorithms are essential to many disciplines because they offer organized, practical answers to challenging issues. Here are a few significant applications of algorithms in various fields:

1. Data Analysis and Machine Learning

Algorithms are used in data analysis and machine learning to find patterns in big datasets and forecast outcomes. Thanks to machine learning methods like support vector machines, decision trees, and neural networks, computers can learn from data and improve over time. These techniques are essential for applications such as recommendation systems, natural language processing, and picture recognition.

2. Cryptography & Security

Cryptography algorithms safeguard data by using encryption and decryption techniques, guaranteeing safe data storage and communication. Algorithms such as SHA-256, AES, and RSA are commonly employed in data integrity maintenance, user authentication, and sensitive information security. These algorithms comprise the foundation of cybersecurity measures used in secure communications, data encryption, and online transactions.

3. Information Retrieval and Search Engines

Search engines can efficiently index and retrieve pertinent information thanks to search algorithms such as PageRank and Hummingbird. By prioritizing web pages according to their significance and relevancy, these algorithms assist users in locating the most relevant information available online. Effective search algorithms are necessary to manage the enormous volume of online information.

4. Optimization problems

Optimization methods are utilized to select the optimal answer from various options. In various industries, including banking, engineering, logistics, and artificial intelligence, sophisticated issues are resolved using methods like gradient descent, linear programming, and genetic algorithms. These algorithms increase productivity, reduce expenses, and optimize resources for operations and decision-making.

5. Genomics and medical diagnostics

Due to their ability to analyze medical images, forecast disease outbreaks, and recognize genetic changes, algorithms are indispensable in medical diagnostics. Personalized medicine has been transformed by machine learning algorithms, in particular, which enable the creation of customized treatment regimens based on each patient's unique genetic profile. Additionally, algorithms help to speed up the sequencing and interpretation of genomic data, improving biotechnology and genomics research.

Characteristics of an Algorithm

An algorithm is a methodical process used to solve a task or solve a problem. Several important factors impact an algorithm's effectiveness:

1. Finiteness

An algorithm must always have a finite number of steps before it ends. When the operation is finished, it must have a defined endpoint or output and not enter an endless loop.

2. Definiteness

An algorithm needs to have exact definitions for each step. Clear and straightforward directions ensure that every step is understood and can be taken easily.

3. Input

An algorithm requires one or more inputs. The values that are first supplied to the algorithm before its processing are known as inputs. These inputs come from a predetermined range of acceptable values.

4. Output

One or more outputs must be produced by an algorithm. The output is the outcome of the algorithm after every step has been completed. The relationship between the input and the result should be clear.

5. Effectiveness

An algorithm's stages must be sufficiently straightforward to be carried out in a finite time utilizing fundamental operations. With the resources at hand, every operation in the algorithm should be doable and practicable.

6. Generality

Rather than being limited to a single particular case, an algorithm should be able to solve a group of issues. It should offer a generic fix that manages a variety of inputs inside a predetermined range or domain.

Types of Algorithms

1. Brute Force Algorithm: A straightforward approach that exhaustively tries all possible solutions, suitable for small problem instances but may become impractical for larger ones due to its high time complexity.

2. Recursive Algorithm: A method that breaks a problem into smaller, similar subproblems and repeatedly applies itself to solve them until reaching a base case, making it effective for tasks with recursive structures.

3. Encryption Algorithm: Utilized to transform data into a secure, unreadable form using cryptographic techniques, ensuring confidentiality and privacy in digital communications and transactions.

4. Backtracking Algorithm: A trial-and-error technique used to explore potential solutions by undoing choices when they lead to an incorrect outcome, commonly employed in puzzles and optimization problems.

5. Searching Algorithm: Designed to find a specific target within a dataset, enabling efficient retrieval of information from sorted or unsorted collections.

6. Sorting Algorithm: Aimed at arranging elements in a specific order, like numerical or alphabetical, to enhance data organization and retrieval.

7. Hashing Algorithm: Converts data into a fixed-size hash value, enabling rapid data access and retrieval in hash tables, commonly used in databases and password storage.

8. Divide and Conquer Algorithm: Breaks a complex problem into smaller subproblems, solves them independently, and then combines their solutions to address the original problem effectively.

9. Greedy Algorithm: Makes locally optimal choices at each step in the hope of finding a global optimum, useful for optimization problems but may not always lead to the best solution.

10. Dynamic Programming Algorithm: Stores and reuses intermediate results to avoid redundant computations, enhancing the efficiency of solving complex problems.

11. Randomized Algorithm: Utilizes randomness in its steps to achieve a solution, often used in situations where an approximate or probabilistic answer suffices.

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How to Write an Algorithm?

• There are no well-defined standards for writing algorithms. It is, however, a problem that is resource-dependent. Algorithms are never written with a specific programming language in mind.
• As you all know, basic code constructs such as loops like do, for, while, all programming languages share flow control such as if-else, and so on. An algorithm can be written using these common constructs.
• Algorithms are typically written in a step-by-step fashion, but this is not always the case. Algorithm writing is a process that occurs after the problem domain has been well-defined. That is, you must be aware of the problem domain for which you are developing a solution.

Example 

Now, use an example to learn how to write algorithms.

Problem: Create an algorithm that multiplies two numbers and displays the output.

Algorithms instruct programmers on how to write code. In addition, the algorithm can be written as:

In algorithm design and analysis, the second method is typically used to describe an algorithm. It allows the analyst to analyze the algorithm while ignoring all unwanted definitions easily. They can see which operations are being used and how the process is progressing. It is optional to write step numbers. To solve a given problem, you create an algorithm. A problem can be solved in a variety of ways.

As a result, many solution algorithms for a given problem can be derived. The following step is to evaluate the proposed solution algorithms and implement the most appropriate solution.

As you progress through this "what is an Algorithm" tutorial, you will learn about some of the components of an algorithm.

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Factors of an Algorithm

The following are the factors to consider when designing an algorithm:

• Modularity: This feature was perfectly designed for the algorithm if you are given a problem and break it down into small-small modules or small-small steps, which is a basic definition of an algorithm.
• Correctness: An algorithm's correctness is defined as when the given inputs produce the desired output, indicating that the algorithm was designed correctly. An algorithm's analysis has been completed correctly.
• Maintainability: It means that the algorithm should be designed in a straightforward, structured way so that when you redefine the algorithm, no significant changes are made to the algorithm.
• Functionality: It takes into account various logical steps to solve a real-world problem.
• Robustness: Robustness refers to an algorithm's ability to define your problem clearly.
• User-friendly: If the algorithm is difficult to understand, the designer will not explain it to the programmer.
• Simplicity: If an algorithm is simple, it is simple to understand.
• Extensibility: Your algorithm should be extensible if another algorithm designer or programmer wants to use it.

You will now see why an algorithm is so essential after understanding some of its components.

Qualities of a Good Algorithm

• Efficiency: A good algorithm should perform its task quickly and use minimal resources.
• Correctness: It must produce the correct and accurate output for all valid inputs.
• Clarity: The algorithm should be easy to understand and comprehend, making it maintainable and modifiable.
• Scalability: It should handle larger data sets and problem sizes without a significant decrease in performance.
• Reliability: The algorithm should consistently deliver correct results under different conditions and environments.
• Optimality: Striving for the most efficient solution within the given problem constraints.
• Robustness: Capable of handling unexpected inputs or errors gracefully without crashing.
• Adaptability: Ideally, it can be applied to a range of related problems with minimal adjustments.
• Simplicity: Keeping the algorithm as simple as possible while meeting its requirements, avoiding unnecessary complexity.

The Complexity of an Algorithm

The algorithm's performance can be measured in two ways:

Time Complexity

The amount of time required to complete an algorithm's execution is called time complexity. The big O notation is used to represent an algorithm's time complexity. The asymptotic notation for describing time complexity, in this case, is big O notation. The time complexity is calculated primarily by counting the number of steps required to complete the execution. Let us look at an example of time complexity.

The time complexity of the loop statement in the preceding code is at least n, and as the value of n escalates, so does the time complexity. While the code's complexity, i.e., returns mul, will be constant because its value is not dependent on the importance of n and will provide the result in a single step. The worst-time complexity is generally considered because it is the maximum time required for any given input size.

Space Complexity

The amount of space an algorithm requires to solve a problem and produce an output is called its space complexity. Space complexity, like time complexity, is expressed in big O notation.

The space is required for an algorithm for the following reasons:

1. To store program instructions.
2. To store track of constant values.
3. To store track of variable values.
4. To store track of function calls, jumping statements, and so on.

Space Complexity = Auxiliary Space + Input Size

Finally after understanding what is an algorithm, its analysis and approches, you will look at different types of algorithms.

Advantage and Disadvantages of Algorithms

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Next Steps

In this tutorial, you learned what an algorithm is and what its characteristics are. After that, you took a look at why you need algorithms, how to write them, and how important they are. After you learned about the approaches and factors of an algorithm, you learned about complexity and types of algorithms.

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