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Understanding Algorithms: The Key to Problem-Solving Mastery

The world of computer science is a fascinating realm, where intricate concepts and technologies continuously shape the way we interact with machines. Among the vast array of ideas and principles, few are as fundamental and essential as algorithms. These powerful tools serve as the building blocks of computation, enabling computers to solve problems, make decisions, and process vast amounts of data efficiently.

An algorithm can be thought of as a step-by-step procedure or a set of instructions designed to solve a specific problem or accomplish a particular task. It represents a systematic approach to finding solutions and provides a structured way to tackle complex computational challenges. Algorithms are at the heart of various applications, from simple calculations to sophisticated machine learning models and complex data analysis.

Understanding algorithms and their inner workings is crucial for anyone interested in computer science. They serve as the backbone of software development, powering the creation of innovative applications across numerous domains. By comprehending the concept of algorithms, aspiring computer science enthusiasts gain a powerful toolset to approach problem-solving and gain insight into the efficiency and performance of different computational methods.

In this article, we aim to provide a clear and accessible introduction to algorithms, focusing on their importance in problem-solving and exploring common types such as searching, sorting, and recursion. By delving into these topics, readers will gain a solid foundation in algorithmic thinking and discover the underlying principles that drive the functioning of modern computing systems. Whether you’re a beginner in the world of computer science or seeking to deepen your understanding, this article will equip you with the knowledge to navigate the fascinating world of algorithms.

What are Algorithms?

At its core, an algorithm is a systematic, step-by-step procedure or set of rules designed to solve a problem or perform a specific task. It provides clear instructions that, when followed meticulously, lead to the desired outcome.

Consider an algorithm to be akin to a recipe for your favorite dish. When you decide to cook, the recipe is your go-to guide. It lists out the ingredients you need, their exact quantities, and a detailed, step-by-step explanation of the process, from how to prepare the ingredients to how to mix them, and finally, the cooking process. It even provides an order for adding the ingredients and specific times for cooking to ensure the dish turns out perfect.

In the same vein, an algorithm, within the realm of computer science, provides an explicit series of instructions to accomplish a goal. This could be a simple goal like sorting a list of numbers in ascending order, a more complex task such as searching for a specific data point in a massive dataset, or even a highly complicated task like determining the shortest path between two points on a map (think Google Maps). No matter the complexity of the problem at hand, there’s always an algorithm working tirelessly behind the scenes to solve it.

Furthermore, algorithms aren’t limited to specific programming languages. They are universal and can be implemented in any language. This is why understanding the fundamental concept of algorithms can empower you to solve problems across various programming languages.

The Importance of Algorithms

Algorithms are indisputably the backbone of all computational operations. They’re a fundamental part of the digital world that we interact with daily. When you search for something on the web, an algorithm is tirelessly working behind the scenes to sift through millions, possibly billions, of web pages to bring you the most relevant results. When you use a GPS to find the fastest route to a location, an algorithm is computing all possible paths, factoring in variables like traffic and road conditions, to provide you the optimal route.

Consider the world of social media, where algorithms curate personalized feeds based on our previous interactions, or in streaming platforms where they recommend shows and movies based on our viewing habits. Every click, every like, every search, and every interaction is processed by algorithms to serve you a seamless digital experience.

In the realm of computer science and beyond, everything revolves around problem-solving, and algorithms are our most reliable problem-solving tools. They provide a structured approach to problem-solving, breaking down complex problems into manageable steps and ensuring that every eventuality is accounted for.

Moreover, an algorithm’s efficiency is not just a matter of preference but a necessity. Given that computers have finite resources — time, memory, and computational power — the algorithms we use need to be optimized to make the best possible use of these resources. Efficient algorithms are the ones that can perform tasks more quickly, using less memory, and provide solutions to complex problems that might be infeasible with less efficient alternatives.

In the context of massive datasets (the likes of which are common in our data-driven world), the difference between a poorly designed algorithm and an efficient one could be the difference between a solution that takes years to compute and one that takes mere seconds. Therefore, understanding, designing, and implementing efficient algorithms is a critical skill for any computer scientist or software engineer.

Hence, as a computer science beginner, you are starting a journey where algorithms will be your best allies — universal keys capable of unlocking solutions to a myriad of problems, big or small.

Common Types of Algorithms: Searching and Sorting

Two of the most ubiquitous types of algorithms that beginners often encounter are searching and sorting algorithms.

Searching algorithms are designed to retrieve specific information from a data structure, like an array or a database. A simple example is the linear search, which works by checking each element in the array until it finds the one it’s looking for. Although easy to understand, this method isn’t efficient for large datasets, which is where more complex algorithms like binary search come in.

Binary search, on the other hand, is like looking up a word in the dictionary. Instead of checking each word from beginning to end, you open the dictionary in the middle and see if the word you’re looking for should be on the left or right side, thereby reducing the search space by half with each step.

Sorting algorithms, meanwhile, are designed to arrange elements in a particular order. A simple sorting algorithm is bubble sort, which works by repeatedly swapping adjacent elements if they’re in the wrong order. Again, while straightforward, it’s not efficient for larger datasets. More advanced sorting algorithms, such as quicksort or mergesort, have been designed to sort large data collections more efficiently.

Diving Deeper: Graph and Dynamic Programming Algorithms

Building upon our understanding of searching and sorting algorithms, let’s delve into two other families of algorithms often encountered in computer science: graph algorithms and dynamic programming algorithms.

A graph is a mathematical structure that models the relationship between pairs of objects. Graphs consist of vertices (or nodes) and edges (where each edge connects a pair of vertices). Graphs are commonly used to represent real-world systems such as social networks, web pages, biological networks, and more.

Graph algorithms are designed to solve problems centered around these structures. Some common graph algorithms include:

Dynamic programming is a powerful method used in optimization problems, where the main problem is broken down into simpler, overlapping subproblems. The solutions to these subproblems are stored and reused to build up the solution to the main problem, saving computational effort.

Here are two common dynamic programming problems:

Understanding these algorithm families — searching, sorting, graph, and dynamic programming algorithms — not only equips you with powerful tools to solve a variety of complex problems but also serves as a springboard to dive deeper into the rich ocean of algorithms and computer science.

Recursion: A Powerful Technique

While searching and sorting represent specific problem domains, recursion is a broad technique used in a wide range of algorithms. Recursion involves breaking down a problem into smaller, more manageable parts, and a function calling itself to solve these smaller parts.

To visualize recursion, consider the task of calculating factorial of a number. The factorial of a number n (denoted as n! ) is the product of all positive integers less than or equal to n . For instance, the factorial of 5 ( 5! ) is 5 x 4 x 3 x 2 x 1 = 120 . A recursive algorithm for finding factorial of n would involve multiplying n by the factorial of n-1 . The function keeps calling itself with a smaller value of n each time until it reaches a point where n is equal to 1, at which point it starts returning values back up the chain.

Algorithms are truly the heart of computer science, transforming raw data into valuable information and insight. Understanding their functionality and purpose is key to progressing in your computer science journey. As you continue your exploration, remember that each algorithm you encounter, no matter how complex it may seem, is simply a step-by-step procedure to solve a problem.

We’ve just scratched the surface of the fascinating world of algorithms. With time, patience, and practice, you will learn to create your own algorithms and start solving problems with confidence and efficiency.

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Three Elegant Algorithms Every Computer Science Beginner Should Know

What is an Algorithm? Algorithm Definition for Computer Science Beginners

If you’re a student and want to study computer science, or you’re learning to code, then there’s a chance you’ve heard of algorithms. Simply put, an algorithm is a set of instructions that performs a particular action.

Contrary to popular belief, an algorithm is not some piece of code that requires extremely advanced knowledge in order to implement. At the same time, I won't say that an algorithm is easy to implement, either. Some can be, but it depends on what you're trying to do.

In the end, the best way to get better at algorithms is by practicing them regularly.

In this article, you'll learn all about algorithms so you'll be prepared next time you encounter one, or have to write one yourself. I will also share some freeCodeCamp resources that will help you learn how to write algorithms in different languages.

What We'll Cover

What exactly is an algorithm.

• Why Do You Need an Algorithm?

Types of Algorithms

• Which Programming Language Is Best for Writing Algorithms? (rename)

Resources for Learning Algorithms

An algorithm is a set of steps for solving a known problem. Most algorithms are implemented to run following the four steps below:

• take an input
• access that input and make sure it's correct
• show the result
• terminate (the stage where the algorithm stop running)

Some steps of the algorithm may run repeatedly, but in the end, termination is what ends an algorithm.

For example, the algorithm below sort numbers in descending order. It loops through the numbers specified until it arranges them in descending order, then terminates when there are no more number to sort:

For a theoretical basis, for instance, an algorithm for dividing two numbers and showing the remainder could run through the steps below:

• Step 1 : the user enters the first and second numbers – the dividend and the divisor
• Step 2 : the algorithm written to perform the division takes in the number, then puts a division sign between the dividend and the divisor. It also checks for a remainder.
• Step 3 : the result of the division and remainder is shown to the user
• Step 4 : the algorithm terminates

Here's how that kind of algorithm is implemented in JavaScript:

If there's an error, the algorithm may not run, or might return the wrong output. If the programmer who wrote the algorithm took user experience into consideration, then an error handler could show an error to the user and let them know what to do.

Why do you Need an Algorithm?

If you’re one of those computer science students asking “why algorithms”, here are some reasons why you should learn about them:

Problem Solving : being able to write an algorithm improves your problem-solving capacity. It is a common belief that once you can solve a problem with one thing, you can solve problems with another closely related one. So, if you can solve problems with Python, you can solve problems with JavaScript.

Scalability : an algorithm helps your software/application/website respond appropriately to demands.

Proper Utilization of Resources: choosing the right algorithm ensures proper utilization of resources such as memory, storage, network, and others.

Algorithms in computer science can be broadly categorized into searching and sorting algorithms:

• Sorting – selection sort, bubble sort, insertion sort, merge sort, quick sort, and so on.
• Searching – binary search, exponential search, jump search, and so on.

But there are many types of algorithms that programers use regularly. Here are some other common algorithm types organized by category:

• Hashing – SHA-256, SHA-1
• Brute force – trial and error
• Divide and conquer – merge sort algorithm
• Greedy – Prim's algorithm, Kruskal's algorithm
• Recursive – computer factorials

Which Programming Language Is Best for Writing Algorithms?

You can write angorithms in any programming language. There's no benefit to using one language over another.

Every language has its strengths and weaknesses, and each has unique syntax and features. So writing an algorithm might look different in one language compared to another.

But algorithms are universal concepts. So if you can write bubble sort in Python, you should also be able to write it in JavaScript or C#.

Here are some videos from the freeCodeCamp YouTube channel that can help you learn algorithms effectively:

• Algorithms and Data Structures Tutorial - Full Course for Beginners
• Algorithms in Python – Full Course for Beginners
• Data Structures Easy to Advanced Course - Full Tutorial from a Google Engineer
• Dynamic Programming - Learn to Solve Algorithmic Problems & Coding Challenges
• Understanding Sorting Algorithms

Also, the interactive JavaScript Algorithms and Data Structures Certification on freeCodeCamp can give you a crash course in algorithmic thinking in JavaScript.

In this article, we went over what an algorithm is, their types, and resources for learning algorithms.

If you read this far, the next thing you should do is start learning algorithms with one or more of the resources listed in this article.

Thank you for reading.

Web developer and technical writer focusing on frontend technologies. I also dabble in a lot of other technologies.

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• What Is an Algorithm? | Definition & Examples

What Is an Algorithm? | Definition & Examples

Published on August 9, 2023 by Kassiani Nikolopoulou . Revised on August 29, 2023.

An algorithm is a set of steps for accomplishing a task or solving a problem. Typically, algorithms are executed by computers, but we also rely on algorithms in our daily lives. Each time we follow a particular step-by-step process, like making coffee in the morning or tying our shoelaces, we are in fact following an algorithm.

In the context of computer science , an algorithm is a mathematical process for solving a problem using a finite number of steps. Algorithms are a key component of any computer program and are the driving force behind various systems and applications, such as navigation systems, search engines, and music streaming services.

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Table of contents

What is an algorithm, how do algorithms work, examples of algorithms, other interesting articles, frequently asked questions about algorithms.

An algorithm is a sequence of instructions that a computer must perform to solve a well-defined problem. It essentially defines what the computer needs to do and how to do it. Algorithms can instruct a computer how to perform a calculation, process data, or make a decision.

The best way to understand an algorithm is to think of it as a recipe that guides you through a series of well-defined actions to achieve a specific goal. Just like a recipe produces a replicable result, algorithms ensure consistent and reliable outcomes for a wide range of tasks in the digital realm.

And just like there are numerous ways to make, for example, chocolate chip cookies by following different steps or using slightly different ingredients, different algorithms can be designed to solve the same problem, with each taking a distinct approach but achieving the same result.

Algorithms are virtually everywhere around us. Examples include the following:

• Search engines rely on algorithms to find and present relevant results as quickly as possible
• Social media platforms use algorithms to prioritize the content that we see in our feeds, taking into account factors like our past behavior, the popularity of posts, and relevance.
• With the help of algorithms, navigation apps determine the most efficient route for us to reach our destination.
• It must be correct . In other words, it should take a given problem and provide the right answer or result, even if it stops working due to an error.
• It must consist of clear, practical steps that can be completed in a limited time, whether by a person or the machine that must execute the algorithm. For example, the instructions in a cookie recipe might be considered sufficiently concrete for a human cook, but they would not be specific enough for programming an automated cookie-making machine.
• There should be no confusion about which step comes next , even if choices must be made (e.g., when using “if” statements).
• It must have a set number of steps (not an infinite number) that can be managed using loops (statements describing repeated actions or iterations).
• It must eventually reach an endpoint and not get stuck in a never-ending loop.

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Algorithms use a set of initial data or input , process it through a series of logical steps or rules, and produce the output (i.e., the outcome, decision, or result).

If you want to make chocolate chip cookies, for instance, the input would be the ingredients and quantities, the process would be the recipe you choose to follow, and the output would be the cookies.

Algorithms are eventually expressed in a programming language that a computer can process. However, when an algorithm is being created, it will be people, not a computer, who will need to understand it. For this reason, as a first step, algorithms are written as plain instructions.

• Input: the input data is a single-digit number (e.g., 5).
• Transformation/processing: the algorithm takes the input (number 5) and performs the specific operation (i.e., multiplies the number by itself).
• Output: the result of the calculation is the square of the input number, which, in this case, would be 25 (since 5 * 5 = 25).

We could express this as an algorithm in the following way:

Algorithm: Calculate the square of a number

• Input the number (N) whose square you want to find.
• Multiply the number (N) by itself.
• Store the result of the multiplication in a variable (result).
• Output the value of the variable (result), which represents the square of the input number.

It is important to keep in mind that an algorithm is not the same as a program or code. It is the logic or plan for solving a problem represented as a simple step-by-step description. Code is the implementation of the algorithm in a specific programming language (like C++ or Python), while a program is an implementation of code that instructs a computer on how to execute an algorithm and perform a task.

Instead of telling a computer exactly what to do, some algorithms allow computers to learn on their own and improve their performance on a specific task. These machine learning algorithms use data to identify patterns and make predictions or conduct data mining to uncover hidden insights in data that can inform business decisions.

Broadly speaking, there are three different types of algorithms:

• Linear sequence algorithms follow a specific set or steps, one after the other. Just like following a recipe, each step depends on the success of the previous one.
• For example, in the context of a cookie recipe, you would include the step “if the dough is too sticky, you might need to refrigerate it.”
• For example, a looping algorithm could be used to handle the process of making multiple cookies from a single batch of dough. The algorithm would repeat a specific set of instructions to form and bake cookies until all the dough has been used.

Algorithms are fundamental tools for problem-solving in both the digital world and many real-life scenarios. Each time we try to solve a problem by breaking it down into smaller, manageable steps, we are in fact using algorithmic thinking.

• Identify which clothes are clean.
• Consider the weather forecast for the day.
• Consider the occasion for which you are getting dressed (e.g., work or school etc.).
• Consider personal preferences (e.g., style or which items match).

In mathematics, algorithms are standard methods for performing calculations or solving equations because they are efficient, reliable, and applicable to various situations.

Suppose you want to add the numbers 345 and 278. You would follow a set of steps (i.e., the standard algorithm for addition):

• Write down the numbers so the digits align.
• Start from the rightmost digits (the ones place) and add them together: 5 + 8 = 13. Write down the 3 and carry over the 1 to the next column.
• Move to the next column (the tens place) and add the digits along with the carried-over value: 4 + 7 + 1 = 12. Write down the 2 and carry over the 1 to the next column.
• Move to the leftmost column (the hundreds place) and add the digits along with the carried-over value: 3 + 2 + 1 = 6. Write down the 6.

The final result is 623

Navigation systems are another example of the use of algorithms. Such systems use algorithms to help you find the easiest and fastest route to your destination while avoiding traffic jams and roadblocks.

If you want to know more about ChatGPT, AI tools , fallacies , and research bias , make sure to check out some of our other articles with explanations and examples.

• ChatGPT vs human editor
• ChatGPT citations
• Is ChatGPT trustworthy?
• Using ChatGPT for your studies
• Sunk cost fallacy
• Straw man fallacy
• Slippery slope fallacy
• Red herring fallacy
• Ecological fallacy
• Logical fallacy

Research bias

• Implicit bias
• Framing bias
• Cognitive bias
• Optimism bias
• Hawthorne effect
• Unconscious bias

In computer science, an algorithm is a list of unambiguous instructions that specify successive steps to solve a problem or perform a task. Algorithms help computers execute tasks like playing games or sorting a list of numbers. In other words, computers use algorithms to understand what to do and give you the result you need.

Algorithms and artificial intelligence (AI) are not the same, however they are closely related.

• Artificial intelligence is a broad term describing computer systems performing tasks usually associated with human intelligence like decision-making, pattern recognition, or learning from experience.
• Algorithms are the instructions that AI uses to carry out these tasks, therefore we could say that algorithms are the building blocks of AI—even though AI involves more advanced capabilities beyond just following instructions.

Algorithms and computer programs are sometimes used interchangeably, but they refer to two distinct but interrelated concepts.

• An algorithm is a step-by-step instruction for solving a problem that is precise yet general.
• Computer programs are specific implementations of an algorithm in a specific programming language. In other words, the algorithm is the high-level description of an idea, while the program is the actual implementation of that idea.

Algorithms are valuable to us because they:

• Form the basis of much of the technology we use in our daily lives, from mobile apps to search engines.
• Power innovations in various industries that augment our abilities (e.g., AI assistants or medical diagnosis).
• Help analyze large volumes of data, discover patterns and make informed decisions in a fast and efficient way, at a scale humans are simply not able to do.
• Automate processes. By streamlining tasks, algorithms increase efficiency, reduce errors, and save valuable time.

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Algorithms in Mathematics

Historical background, repeatability, optimization, scalability, generalization, flexibility, verification, definiteness, feasibility, effectiveness, clarity and simplicity, search engines, social media, recommendation systems, image and speech recognition, data compression, cryptography, financial trading, transportation, algorithm|definition & meaning.

An algorithm is a well-defined sequence of steps focused on solving a particular problem. It is like a recipe, except the dish you are cooking is a mathematical one. Long division is one algorithm to find the quotient of any division operation. It even has branching steps dealing with all the situations that you see! Computers function completely on man-made algorithms.

An algorithm is a series of guidelines or procedures that are carried out in a certain sequence in order to solve a problem or achieve a goal. It is a detailed process for finishing a particular activity or problem. 

Algorithms are used in computer programming and mathematics, as well as in many other fields, such as operations research and artificial intelligence. The concept of the algorithm is depicted in Figure 1 below:

Figure 1: Concept of algorithm

An algorithm can be easy or complicated, and it can be stated in a variety of ways, including as natural language, pseudo code, or code written in a specific programming language.

In mathematics, an algorithm refers to a set of well-defined instructions for performing a specific task or solving a specific problem. These instructions can be in the form of a formula, a set of steps, or a procedure.

Algorithms are used to help solve mathematical problems, such as finding the greatest common divisor of two numbers or solving a system of equations.

The Euclidean algorithm, used in mathematics to determine the greatest common divisor (GCD) of two numbers, is an illustration of an algorithm. The algorithm is as follows:

• Take two numbers, a and b.
• Divide a by b and get the remainder, r .
• If r is zero , then b is the GCD.
• If r is not zero , then repeat steps using b and r .

The GCD of two integers is equivalent to the GCD of the smaller number, and the remainder after the bigger number is divided by, the smaller number, according to the Euclidean algorithm. By using this algorithm repeatedly, the GCD of two numbers can be found efficiently. One such example of computing HCF using the Euclidean algorithm is shown below in Figure 2.

Figure 2: Euclidean Algorithm

The concept of algorithms has been around for centuries, dating back to ancient times. The earliest known algorithm is the Euclidean algorithm, which was described by the Greek mathematician Euclid around 300 BCE. This algorithm is used to find the greatest common divisor (GCD) of two numbers and is still in use today.

Al-Khwarizmi, an Arab mathematician, authored “The Compendious Book on Calculation by Completion and Balancing” during the Middle Ages, which introduced the concept of algorithms for solving mathematical problems.

The word “algorithm” is derived from the Latinized version of his name, Algoritmi. During the Renaissance, the concept of algorithms was further developed by mathematicians such as Fibonacci, who introduced the Fibonacci sequence and the algorithm for finding it.

Algorithms were also used in the design of mechanical calculators and other devices. In the 19th century, Charles Babbage designed the Analytical Engine, which was considered to be the first general-purpose computer. Babbage’s design included a concept of algorithms, which were used to control the operation of the machine.

The invention of electronic computers in the 20th century greatly increased the use of algorithms. Algorithms were used to solve a wide range of problems, from simple calculations to complex scientific and engineering problems. The field of computer science was established, and the study of algorithms became an important part of it.

The concept of algorithms also became crucial in the field of artificial intelligence, as it is used to develop efficient and effective solutions for problems such as speech recognition, image analysis, and natural language processing.

Overall, the history of algorithms is a long one, and it is deeply rooted in mathematics, computer science, and other fields; It will continue to be a key idea in resolving issues and bettering our world because it is evolving and playing a significant part in many facets of our life.

Advantages of Algorithms

Algorithms have many advantages; some of the most notable include the following:

Algorithms are designed to be efficient in terms of time and space complexity, meaning they can solve problems quickly and use minimal resources.

Algorithms can be repeated multiple times to produce the same result, making them reliable and consistent.

Algorithms can be implemented as computer programs, which can perform tasks automatically and without human intervention.

Algorithms can be used to optimize a solution to a problem by finding the best possible solution among a set of alternatives.

Algorithms can be designed to handle large input sets and scale up to accommodate more data or increased complexity.

Algorithms can be designed to be general and apply to a wide range of problem instances rather than being specific to a single problem.

Algorithms can be modified or combined to solve different types of problems or to improve performance.

Algorithms can be verified for their correctness, and their performance can be analyzed and improved.

Algorithms play a vital role in many aspects of our lives, from simple calculations to complex scientific and engineering problems. They are fundamental to the functioning of computers and other digital devices , and they are employed in numerous industries, including finance, healthcare, transportation, and a host of others.

Properties of Algorithms

Algorithms have several properties that are important to consider when designing or analyzing them. Some of these properties include:

Algorithms take input in the form of one or more data sets, which can be in various forms, such as numbers, strings, or other data structures.

Algorithms produce output, which can be in the form of a single value, a set of values, or a more complex data structure.

Algorithms have a clear set of instructions that are well-defined and unambiguous.

Algorithms terminate after a finite number of steps, meaning they will always produce an output.

Algorithms can be executed by a computer or other machine, meaning they are physically possible to implement.

Despite being exclusive to a single problem, algorithms can be used to solve many other types of problems.

Algorithms should be able to produce the correct output for any input within the specified range.

Algorithms can be analyzed in terms of time and space complexity, which measures the number of resources required to execute the algorithm.

Algorithms can be designed to find the optimal solution amid a range of options or to identify a nearly perfect answer.

Algorithms should be easy to understand and implement, so they should be clear and simple as possible.

It’s important to note that an algorithm can have different properties and trade-offs, and choosing the right algorithm for a particular problem depends on the desired properties. Algorithm design and analysis is a complex field, and the properties of algorithms are subject to continuous research and development.

Algorithms in the Real World

Some of the real-world applications of the concept of the algorithm are shown in Figure 3 below:

Figure 3: Real-life applications of the algorithm

Algorithms are used in many different fields and industries, and they have a wide range of applications in the real world. Some examples of algorithms in the real world include:

Algorithms are used to determine the relevance and ranking of web pages when a user performs a search.

Algorithms are used to determine which posts and content will be shown to users in their news feeds.

Algorithms are used to make personalized recommendations for products, music, movies, and other content.

Algorithms are used to analyze and interpret images and speech, which are used in applications such as self-driving cars, facial recognition, and voice-controlled assistants.

Algorithms are used to reduce the size of data files, making them more efficient to store and transmit.

Algorithms are used to secure communications by encrypting and decrypting messages.

Recommendations and decisions about the purchase and sale of stocks, currencies, commodities, and other financial derivatives are made using algorithms.

Algorithms are used to analyze patient data and make treatment recommendations, as well as to identify potential outbreaks of infectious diseases.

Algorithms are used to optimize routes for delivery trucks and other vehicles, as well as to manage traffic flow in cities.

Algorithms are used to control the behavior of robots and drones, enabling them to navigate, manipulate objects and make decisions.

These are only a few instances of the numerous applications of algorithms in the real world. Algorithms continue to play an increasingly important role in our daily lives, and their applications will continue to expand in the future.

Example of Creating an Algorithm

Create an algorithm to evaluate if an integer is odd or even.

The algorithm to determine whether an integer is odd or even is:

Step 1: We divide the integer by 2. Step 2: The integer is even if it can be divided evenly by 2; otherwise, it is odd.

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Ideas Made to Matter

How to use algorithms to solve everyday problems

Kara Baskin

May 8, 2017

How can I navigate the grocery store quickly? Why doesn’t anyone like my Facebook status? How can I alphabetize my bookshelves in a hurry? Apple data visualizer and MIT System Design and Management graduate Ali Almossawi solves these common dilemmas and more in his new book, “ Bad Choices: How Algorithms Can Help You Think Smarter and Live Happier ,” a quirky, illustrated guide to algorithmic thinking. 

For the uninitiated: What is an algorithm? And how can algorithms help us to think smarter?

An algorithm is a process with unambiguous steps that has a beginning and an end, and does something useful.

Algorithmic thinking is taking a step back and asking, “If it’s the case that algorithms are so useful in computing to achieve predictability, might they also be useful in everyday life, when it comes to, say, deciding between alternative ways of solving a problem or completing a task?” In all cases, we optimize for efficiency: We care about time or space.

Note the mention of “deciding between.” Computer scientists do that all the time, and I was convinced that the tools they use to evaluate competing algorithms would be of interest to a broad audience.

Why did you write this book, and who can benefit from it?

All the books I came across that tried to introduce computer science involved coding. My approach to making algorithms compelling was focusing on comparisons. I take algorithms and put them in a scene from everyday life, such as matching socks from a pile, putting books on a shelf, remembering things, driving from one point to another, or cutting an onion. These activities can be mapped to one or more fundamental algorithms, which form the basis for the field of computing and have far-reaching applications and uses.

I wrote the book with two audiences in mind. One, anyone, be it a learner or an educator, who is interested in computer science and wants an engaging and lighthearted, but not a dumbed-down, introduction to the field. Two, anyone who is already familiar with the field and wants to experience a way of explaining some of the fundamental concepts in computer science differently than how they’re taught.

I’m going to the grocery store and only have 15 minutes. What do I do?

Do you know what the grocery store looks like ahead of time? If you know what it looks like, it determines your list. How do you prioritize things on your list? Order the items in a way that allows you to avoid walking down the same aisles twice.

For me, the intriguing thing is that the grocery store is a scene from everyday life that I can use as a launch pad to talk about various related topics, like priority queues and graphs and hashing. For instance, what is the most efficient way for a machine to store a prioritized list, and what happens when the equivalent of you scratching an item from a list happens in the machine’s list? How is a store analogous to a graph (an abstraction in computer science and mathematics that defines how things are connected), and how is navigating the aisles in a store analogous to traversing a graph?

Nobody follows me on Instagram. How do I get more followers?

The concept of links and networks, which I cover in Chapter 6, is relevant here. It’s much easier to get to people whom you might be interested in and who might be interested in you if you can start within the ball of links that connects those people, rather than starting at a random spot.

You mention Instagram: There, the hashtag is one way to enter that ball of links. Tag your photos, engage with users who tag their photos with the same hashtags, and you should be on your way to stardom.

What are the secret ingredients of a successful Facebook post?

I’ve posted things on social media that have died a sad death and then posted the same thing at a later date that somehow did great. Again, if we think of it in terms that are relevant to algorithms, we’d say that the challenge with making something go viral is really getting that first spark. And to get that first spark, a person who is connected to the largest number of people who are likely to engage with that post, needs to share it.

With [my first book], “Bad Arguments,” I spent a month pouring close to $5,000 into advertising for that project with moderate results. And then one science journalist with a large audience wrote about it, and the project took off and hasn’t stopped since.

What problems do you wish you could solve via algorithm but can’t?

When we care about efficiency, thinking in terms of algorithms is useful. There are cases when that’s not the quality we want to optimize for — for instance, learning or love. I walk for several miles every day, all throughout the city, as I find it relaxing. I’ve never asked myself, “What’s the most efficient way I can traverse the streets of San Francisco?” It’s not relevant to my objective.

Algorithms are a great way of thinking about efficiency, but the question has to be, “What approach can you optimize for that objective?” That’s what worries me about self-help: Books give you a silver bullet for doing everything “right” but leave out all the nuances that make us different. What works for you might not work for me.

Which companies use algorithms well?

When you read that the overwhelming majority of the shows that users of, say, Netflix, watch are due to Netflix’s recommendation engine, you know they’re doing something right.

Related Articles

What is Problem Solving Algorithm?, Steps, Representation

What is Problem Solving Algorithm?

Computers are used for solving various day-to-day problems and thus problem solving is an essential skill that a computer science student should know. It is pertinent to mention that computers themselves cannot solve a problem. Precise step-by-step instructions should be given by us to solve the problem.

Thus, problem solving is the process of identifying a problem, developing an algorithm for the identified problem and finally implementing the algorithm to develop a computer program.

Definition of Problem Solving Algorithm

[su_quote cite=””]Algorithms are the solutions to computational problems. They define a method that uses the input to a problem in order to produce the correct output. A computational problem can have many solutions.[/su_quote]

[su_quote cite=””]An algorithm is defined as a step-by-step procedure designed to perform an operation which will lead to the desired result, if followed correctly.[/su_quote]

[su_quote cite=””]Algorithms have a definite beginning and a definite end, and a finite number of steps. A good algorithm, which is precise, unique and finite, receives input and produces an output.[/su_quote]

Steps for Problem Solving

When problems are straightforward and easy, we can easily find the solution. But a complex problem requires a methodical approach to find the right solution. In other words, we have to apply problem solving techniques.

Problem solving begins with the precise identification of the problem and ends with a complete working solution in terms of a program or software. Key steps required for solving a problem using a computer.

Following are Steps for Problem Solving :

Analysing the Problem

It is important to clearly understand a problem before we begin to find the solution for it. If we are not clear as to what is to be solved, we may end up developing a program which may not solve our purpose.

Developing an Algorithm

It is essential to device a solution before writing a program code for a given problem. The solution is represented in natural language and is called an algorithm. We can imagine an algorithm like a very well-written recipe for a dish, with clearly defined steps that, if followed, one will end up preparing the dish.

We start with a tentative solution plan and keep on refining the algorithm until the algorithm is able to capture all the aspects of the desired solution. For a given problem, more than one algorithm is possible and we have to select the most suitable solution.

Testing and Debugging

Software industry follows standardised testing methods like unit or component testing, integration testing, system testing, and acceptance testing while developing complex applications. This is to ensure that the software meets all the business and technical requirements and works as expected.

The maintenance of the solution, thus, involves fixing the problems faced by the user, answering the queries of the user and even serving the request for addition or modification of features.

Representation of Algorithms

Using their algorithmic thinking skills, the software designers or programmers analyse the problem and identify the logical steps that need to be followed to reach a solution. Once the steps are identified, the need is to write down these steps along with the required input and desired output.

A flow chart is a step by step diagrammatic representation of the logic paths to solve a given problem. Or A flowchart is visual or graphical representation of an algorithm .

The flowcharts are pictorial representation of the methods to b used to solve a given problem and help a great deal to analyze the problem and plan its solution in a systematic and orderly manner. A flowchart when translated in to a proper computer language, results in a complete program.

Advantages of Flowcharts:

Differences between algorithm and flowchart.

Pseudo code

In pseudo code , the program is represented in terms of words and phrases, but the syntax of program is not strictly followed.

Advantages of Pseudocode

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Overview of the Problem-Solving Mental Process

• Identify the Problem
• Define the Problem
• Form a Strategy
• Organize Information
• Allocate Resources
• Monitor Progress
• Evaluate the Results

Frequently Asked Questions

Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue.

The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything they can about the issue and then using factual knowledge to come up with a solution. In other instances, creativity and insight are the best options.

It is not necessary to follow problem-solving steps sequentially, It is common to skip steps or even go back through steps multiple times until the desired solution is reached.

In order to correctly solve a problem, it is often important to follow a series of steps. Researchers sometimes refer to this as the problem-solving cycle. While this cycle is portrayed sequentially, people rarely follow a rigid series of steps to find a solution.

The following steps include developing strategies and organizing knowledge.

1. Identifying the Problem

While it may seem like an obvious step, identifying the problem is not always as simple as it sounds. In some cases, people might mistakenly identify the wrong source of a problem, which will make attempts to solve it inefficient or even useless.

Some strategies that you might use to figure out the source of a problem include :

• Asking questions about the problem
• Breaking the problem down into smaller pieces
• Looking at the problem from different perspectives
• Conducting research to figure out what relationships exist between different variables

2. Defining the Problem

After the problem has been identified, it is important to fully define the problem so that it can be solved. You can define a problem by operationally defining each aspect of the problem and setting goals for what aspects of the problem you will address

At this point, you should focus on figuring out which aspects of the problems are facts and which are opinions. State the problem clearly and identify the scope of the solution.

3. Forming a Strategy

After the problem has been identified, it is time to start brainstorming potential solutions. This step usually involves generating as many ideas as possible without judging their quality. Once several possibilities have been generated, they can be evaluated and narrowed down.

The next step is to develop a strategy to solve the problem. The approach used will vary depending upon the situation and the individual's unique preferences. Common problem-solving strategies include heuristics and algorithms.

• Heuristics are mental shortcuts that are often based on solutions that have worked in the past. They can work well if the problem is similar to something you have encountered before and are often the best choice if you need a fast solution.
• Algorithms are step-by-step strategies that are guaranteed to produce a correct result. While this approach is great for accuracy, it can also consume time and resources.

Heuristics are often best used when time is of the essence, while algorithms are a better choice when a decision needs to be as accurate as possible.

4. Organizing Information

Before coming up with a solution, you need to first organize the available information. What do you know about the problem? What do you not know? The more information that is available the better prepared you will be to come up with an accurate solution.

When approaching a problem, it is important to make sure that you have all the data you need. Making a decision without adequate information can lead to biased or inaccurate results.

5. Allocating Resources

Of course, we don't always have unlimited money, time, and other resources to solve a problem. Before you begin to solve a problem, you need to determine how high priority it is.

If it is an important problem, it is probably worth allocating more resources to solving it. If, however, it is a fairly unimportant problem, then you do not want to spend too much of your available resources on coming up with a solution.

At this stage, it is important to consider all of the factors that might affect the problem at hand. This includes looking at the available resources, deadlines that need to be met, and any possible risks involved in each solution. After careful evaluation, a decision can be made about which solution to pursue.

6. Monitoring Progress

After selecting a problem-solving strategy, it is time to put the plan into action and see if it works. This step might involve trying out different solutions to see which one is the most effective.

It is also important to monitor the situation after implementing a solution to ensure that the problem has been solved and that no new problems have arisen as a result of the proposed solution.

Effective problem-solvers tend to monitor their progress as they work towards a solution. If they are not making good progress toward reaching their goal, they will reevaluate their approach or look for new strategies .

7. Evaluating the Results

After a solution has been reached, it is important to evaluate the results to determine if it is the best possible solution to the problem. This evaluation might be immediate, such as checking the results of a math problem to ensure the answer is correct, or it can be delayed, such as evaluating the success of a therapy program after several months of treatment.

Once a problem has been solved, it is important to take some time to reflect on the process that was used and evaluate the results. This will help you to improve your problem-solving skills and become more efficient at solving future problems.

A Word From Verywell​

It is important to remember that there are many different problem-solving processes with different steps, and this is just one example. Problem-solving in real-world situations requires a great deal of resourcefulness, flexibility, resilience, and continuous interaction with the environment.

Get Advice From The Verywell Mind Podcast

Hosted by therapist Amy Morin, LCSW, this episode of The Verywell Mind Podcast shares how you can stop dwelling in a negative mindset.

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You can become a better problem solving by:

• Practicing brainstorming and coming up with multiple potential solutions to problems
• Being open-minded and considering all possible options before making a decision
• Breaking down problems into smaller, more manageable pieces
• Asking for help when needed
• Researching different problem-solving techniques and trying out new ones
• Learning from mistakes and using them as opportunities to grow

It's important to communicate openly and honestly with your partner about what's going on. Try to see things from their perspective as well as your own. Work together to find a resolution that works for both of you. Be willing to compromise and accept that there may not be a perfect solution.

Take breaks if things are getting too heated, and come back to the problem when you feel calm and collected. Don't try to fix every problem on your own—consider asking a therapist or counselor for help and insight.

If you've tried everything and there doesn't seem to be a way to fix the problem, you may have to learn to accept it. This can be difficult, but try to focus on the positive aspects of your life and remember that every situation is temporary. Don't dwell on what's going wrong—instead, think about what's going right. Find support by talking to friends or family. Seek professional help if you're having trouble coping.

Davidson JE, Sternberg RJ, editors.  The Psychology of Problem Solving .  Cambridge University Press; 2003. doi:10.1017/CBO9780511615771

Sarathy V. Real world problem-solving .  Front Hum Neurosci . 2018;12:261. Published 2018 Jun 26. doi:10.3389/fnhum.2018.00261

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

An Algorithm Is Defined As

An algorithm is defined as:

A) a mathematical formula that solves a problem. B) a tempo for classical music played in a coda. C) a logical sequence of steps that solve a problem. D) a tool that designs computer programs and draws the user interface.

Correct Answer:

Unlock this answer now Get Access to more Verified Answers free of charge.

Q1: Pseudocode is A) data that have been encoded

Q2: When starting a new program, it is

Q3: Suppose in the very early stages of

Q4: Which of the following is NOT considered

Q5: Software refers to the people who work

Q7: Although the documentation step is usually listed

Q8: Visual Basic uses a graphical user interface.

Q9: The person who actually runs a computer

Q10: Often a problem is too difficult to

Q11: In most cases, a well-written program need

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7. Thinking and Intelligence

Problem solving, learning objectives.

By the end of this section, you will be able to:

• Describe problem solving strategies
• Define algorithm and heuristic
• Explain some common roadblocks to effective problem solving

People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

PROBLEM-SOLVING STRATEGIES

When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them ( [link] ). For example, a well-known strategy is trial and error . The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below ( [link] ) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

How long did it take you to solve this sudoku puzzle? (You can see the answer at the end of this section.)

Here is another popular type of puzzle ( [link] ) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Did you figure it out? (The answer is at the end of this section.) Once you understand how to crack this puzzle, you won’t forget.

Take a look at the “Puzzling Scales” logic puzzle below ( [link] ). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

PITFALLS TO PROBLEM SOLVING

Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

Link to Learning

Check out this Apollo 13 scene where the group of NASA engineers are given the task of overcoming functional fixedness.

Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An anchoring bias occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in [link] .

Please visit this site to see a clever music video that a high school teacher made to explain these and other cognitive biases to his AP psychology students.

Were you able to determine how many marbles are needed to balance the scales in [link] ? You need nine. Were you able to solve the problems in [link] and [link] ? Here are the answers ( [link] ).

Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. Roadblocks to problem solving include a mental set, functional fixedness, and various biases that can cloud decision making skills.

Self Check Questions

Critical thinking questions.

1. What is functional fixedness and how can overcoming it help you solve problems?

2. How does an algorithm save you time and energy when solving a problem?

Personal Application Question

3. Which type of bias do you recognize in your own decision making processes? How has this bias affected how you’ve made decisions in the past and how can you use your awareness of it to improve your decisions making skills in the future?

1. Functional fixedness occurs when you cannot see a use for an object other than the use for which it was intended. For example, if you need something to hold up a tarp in the rain, but only have a pitchfork, you must overcome your expectation that a pitchfork can only be used for garden chores before you realize that you could stick it in the ground and drape the tarp on top of it to hold it up.

2. An algorithm is a proven formula for achieving a desired outcome. It saves time because if you follow it exactly, you will solve the problem without having to figure out how to solve the problem. It is a bit like not reinventing the wheel.

• Psychology. Authored by : OpenStax College. Located at : http://cnx.org/contents/[email protected]:1/Psychology . License : CC BY: Attribution . License Terms : Download for free at http://cnx.org/content/col11629/latest/.

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Application of neural network algorithm in computer data information processing system

• Cao, Xiuping

In order to solve the problem that the data processing frequency of the traditional centralized data processing system is low, which leads to the poor feedback effect of massive data, this paper proposes a computer data information processing system based on neural network algorithm. Based on the original system hardware, the system replaces the data processor and increases the total number of the processor, so as to realize the distributed synchronous processing of massive data. In the aspect of software design, the interaction between system units and modules is formed through the protocol to improve the data communication mode of the system; Calculate the Euclidean distance and set the distributed processing mode of the system; According to the definition of cloud computing, the classification function is used to determine the constraints, establish the processing frequency equation, and realize the rapid processing of massive data. The experimental results show that the processing frequency of the system designed in this paper is more than 50 % higher than that of the traditional system, which solves the problem of low processing frequency caused by weak analysis ability and slow response speed in the traditional system. compared with the traditional system, the system designed in this paper has faster processing frequency for massive data and better feedback effect to users.

• Distributed network;
• Data processing;
• System design;
• Cloud computing technology;
• Establishment of treatment equation;
• Comparative verification