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It is a standard protocol to use maps and blueprints for executing various processes smoothly. Just like an architect uses detailed blueprints to establish a colossal building from scratch, software engineers and data science experts use different algorithms to solve a wide range of computational problems. This is what is algorithm in computer programming does.
Computers are incapable of functioning independently. Even the simplest tasks, such as mathematical equations involving summation or multiplication, require algorithms, making them essential for digital devices. Algorithms form the base of any computing system, dedicating sequences of commands to accomplish tasks such as calculating, programming or data processing. These algorithms have equipped computers to process information, solve complex equations, etc., almost minimizing human labor for routine tasks. This is what is algorithm in programming.
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Today, we’ll take an in-depth look into what algorithms mean for computer science and data science.
What is an Algorithm?
To put it simply, an algorithm is a precise manual for a computer, including a step-by-step process of accomplishing input tasks to deliver the expected output. This solution-seeking process consists of a finite sequence of instructions to let the computer know how to deal with an issue and deliver output accordingly.
The development of new algorithms has sped up the computing process. However, you must know which algorithm to choose for which task. Essentially, an algorithm can be a digital map designed to solve logical and mathematical equations in natural languages.
Characteristics of Algorithm
An algorithm has various characteristics, such as –
- Input- An algorithm can be given a value other than 0 as an input.
- Output- There will be one or more outcomes at the end of an algorithm.
- Unambiguity- A perfect algorithm is usually defined as unambiguous.
- Finiteness- An algorithm should be finite, which means that an algorithm must have a limited number of instructions.
- Effectiveness- The algorithm should be adequate as it affects the overall process.
Importance of Algorithm
Being the backbone of computing processes, algorithms narrate the best and easiest methods to solve a problem, reaching its excellent result. From increasing software’s accuracy by strengthening the source program to concluding tasks using limited resources (like memory power), algorithms can enhance the efficiency of computational processes and software.
Simplified variants of algorithms broken into smaller steps help programmers to understand and create efficient computer programs. Algorithms are language-independent, accepting the steps to be executed in any language but leading towards the desired result.
Properties of an Algorithm
Not every written sequence of instructions is an algorithm. Several features make the set of instructions eligible as an algorithm. For a series of instructions to be considered an algorithm, these are must-own features.
- Input: An algorithm must carry well-defined inputs, usually more than one.
- Output: Expected output must be clearly defined by the algorithm.
- Unambiguous: The written algorithm must not be ambiguous at all. It should have the right steps for programmers to follow and create effective results.
- Definite: Instead of infinite loops and each step carrying dual meaning, definite algorithms must carry only a single interpretation with each statement.
- Finite: To reach an effective solution, algorithm steps must be limited and well-defined.
- Practical: An algorithm should align with the existing resources to stay efficient while making the most out of it.
Diverse Ways to Represent Algorithms
Algorithms can be represented in four different ways. These include:
1. Natural Language
Regularly exchanged natural languages such as English, French, Spanish, etc., can also represent algorithms. As long as the programmer understands the neatly drawn steps, it is acceptable. However, natural languages often lack the precision that computer programs demand, and hence, they are not widely preferred.
2. Pseudocode
This informal method to represent an algorithm uses natural language minus its ambiguous parts. Pseudocode delivers instructions in the most precise form.
3. Flow Charts
The chart represents programming in a flow-like format to simplify understanding. Illustration of algorithms in this graphical format uses different symbols and boxes, limiting interpretations while delivering exact meaning.
4. Programming Languages
Algorithms written in programming languages are ready to be turned into a program. With a few changes and corrections, algorithms written in a programming language are ready to go.
What is an Algorithm in Programming?
Besides solving basic computational commands, algorithms help in software development by executing simple and complex directions. The algorithm plays a significant role in programming as it is used in the designing phase and preceded by its implementation in the project under the programming phase.
To understand it better, algorithms work as code to guide a software program to accomplish particular tasks. For example, Google displays the most relevant search results based on the detailed algorithms to find, rank, and display the most relevant pages to the search keyword. Lack of the right algorithms might lead to jumbled, irrelevant page results.
Role of Algorithms in AI
Artificial Intelligence is based on mimicking human-like intelligence to refine delivering breakthrough personalized and user-friendly services. Integration of AI in daily life has simplified many tasks, right from suggesting products relevant to our search algorithm to monitoring our vitals! A life without AI in the 21st century is hard to envision, but what’s the technology behind it?
AI cannot access information on its own. Just like we slowly understand how the world works and work in accordance with it, AI has to learn to mimic human intelligence through basic instructions. This is where algorithms enter the scene.
Algorithms are essentially precise instructions that train the AI to read, analyze, and perform relevant tasks and processes. An AI algorithm is a subset of machine learning that prepares a d evice to mimic human intellect and work out tasks independently. One good example is smart virtual assistants that play songs on a single command or recommend playlists solely based on your search history and interest pages. What’s more, these assistants can also differentiate between various voices – imagine the complexity level of the algorithms used to make this possible!.
Why do we need an algorithm?
There are various reasons we need an algorithm, and scalability and performance are one of those. When there is an actual real-world problem, it must be broken down into small steps to analyse it quickly. On the other hand, if performance can be broken down into smaller steps, it determines that the problem is feasible.
Factors of an algorithm
- Modularity- This feature is perfectly designed to break down a problem into small-small modules or steps.
- Correctness- It is defined as when the given input produces the desired output.
- Maintainability: The algorithm should be designed in a straightforward and structured manner.
- Functionality- It takes into account various logical steps to solve a real-world problem.
- Extensibility: The algorithm should be extensible if the algorithm designer wants to use it.
Types of Algorithms
There are various types of algorithms used to solve computation problems through varying methods. The most prominently used ones are:
1. Backtracking Algorithm
This algorithm divides and solves the subproblem of any equation. For instance, it tries to solve a problem, and if it fails to solve, it backtracks by removing the last step and then again tries to solve the problem.
2. Brute Force Algorithm
This algorithm iterates all possible solutions, just like we do, and finds the most relevant one to solve the problem. It is one of the most simple algorithms.
3. Divide and Conquer Algorithm
As the name implies, this algorithm divides a problem into two parts. It then further tries to solve the first half in smaller subproblems and does the same with the other half, only to combine all solutions to create the resultant solution.
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4. Dynamic Programming Algorithm
Dynamic programming algorithms use past data to analyze and solve a given problem. It breaks a problem into subproblems, decrypts every part of it, and then stores the pattern for future use.
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5. Recursive Algorithm
The recursive algorithm solves the problem by breaking it into subproblems of similar types and then calls itself repeatedly until the problem is solved in its entirety.
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6. Greedy Algorithm
Greedy algorithms follow a problem-solving pattern that works part by part and goes with the solution that delivers immediate profit. It aims to find the most locally optimal solution, yet it often fails to find it as it does not go through the entire data.
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Conclusion
The bottom line – algorithms are the building blocks of all computing processes, be it a simple computer program or an ML program. Algorithms are crucial for all computerized devices since they can automate different tasks, perform repetitive tasks efficiently, and reduce the reliance on manual labor, thereby streamlining the entire operation. They can save both time and money.
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