# What is a plain English explanation of "Big O" notation?

**5.3k**

I'd prefer as little formal definition as possible and simple mathematics.

I'd prefer as little formal definition as possible and simple mathematics.

The answer provides a clear and concise explanation of Big Oh notation in plain English. It also explains the concept of best case, worst case, and average case scenarios.

Quick note, my answer is almost certainly confusing Big Oh notation (which is an upper bound) with Big Theta notation "Θ" (which is a two-side bound). But in my experience, this is actually typical of discussions in non-academic settings. Apologies for any confusion caused.

BigOh complexity can be visualized with this graph: The simplest definition I can give for Big Oh notation is this:

There are some important and deliberately chosen words in that sentence:

Come back and reread the above when you've read the rest. The best example of BigOh I can think of is doing arithmetic. Take two numbers (123456 and 789012). The basic arithmetic operations we learned in school were:

Each of these is an operation or a problem. A method of solving these is called an . The addition is the simplest. You line the numbers up (to the right) and add the digits in a column writing the last number of that addition in the result. The 'tens' part of that number is carried over to the next column. Let's assume that the addition of these numbers is the most expensive operation in this algorithm. It stands to reason that to add these two numbers together we have to add together 6 digits (and possibly carry a 7th). If we add two 100 digit numbers together we have to do 100 additions. If we add 10,000 digit numbers we have to do 10,000 additions. See the pattern? The (being the number of operations) is directly proportional to the number of digits in the larger number. We call this or . Subtraction is similar (except you may need to borrow instead of carry). Multiplication is different. You line the numbers up, take the first digit in the bottom number and multiply it in turn against each digit in the top number and so on through each digit. So to multiply our two 6 digit numbers we must do 36 multiplications. We may need to do as many as 10 or 11 column adds to get the end result too. If we have two 100-digit numbers we need to do 10,000 multiplications and 200 adds. For two one million digit numbers we need to do one trillion (10) multiplications and two million adds. As the algorithm scales with n-, this is or . This is a good time to introduce another important concept:

The astute may have realized that we could express the number of operations as: n + 2n. But as you saw from our example with two numbers of a million digits apiece, the second term (2n) becomes insignificant (accounting for 0.0002% of the total operations by that stage). One can notice that we've assumed the worst case scenario here. While multiplying 6 digit numbers, if one of them has 4 digits and the other one has 6 digits, then we only have 24 multiplications. Still, we calculate the worst case scenario for that 'n', i.e when both are 6 digit numbers. Hence Big Oh notation is about the Worst-case scenario of an algorithm.

The next best example I can think of is the telephone book, normally called the White Pages or similar but it varies from country to country. But I'm talking about the one that lists people by surname and then initials or first name, possibly address and then telephone numbers. Now if you were instructing a computer to look up the phone number for "John Smith" in a telephone book that contains 1,000,000 names, what would you do? Ignoring the fact that you could guess how far in the S's started (let's assume you can't), what would you do? A typical implementation might be to open up to the middle, take the 500,000 and compare it to "Smith". If it happens to be "Smith, John", we just got really lucky. Far more likely is that "John Smith" will be before or after that name. If it's after we then divide the last half of the phone book in half and repeat. If it's before then we divide the first half of the phone book in half and repeat. And so on. This is called a and is used every day in programming whether you realize it or not. So if you want to find a name in a phone book of a million names you can actually find any name by doing this at most 20 times. In comparing search algorithms we decide that this comparison is our 'n'.

That is staggeringly good, isn't it? In BigOh terms this is or . Now the logarithm in question could be ln (base e), log, log or some other base. It doesn't matter it's still O(log n) just like O(2n) and O(100n) are still both O(n). It's worthwhile at this point to explain that BigOh can be used to determine three cases with an algorithm:

Normally we don't care about the best case. We're interested in the expected and worst case. Sometimes one or the other of these will be more important. Back to the telephone book. What if you have a phone number and want to find a name? The police have a reverse phone book but such look-ups are denied to the general public. Or are they? Technically you can reverse look-up a number in an ordinary phone book. How? You start at the first name and compare the number. If it's a match, great, if not, you move on to the next. You have to do it this way because the phone book is (by phone number anyway). So to find a name given the phone number (reverse lookup):

This is quite a famous problem in computer science and deserves a mention. In this problem, you have N towns. Each of those towns is linked to 1 or more other towns by a road of a certain distance. The Traveling Salesman problem is to find the shortest tour that visits every town. Sounds simple? Think again. If you have 3 towns A, B, and C with roads between all pairs then you could go:

Well, actually there's less than that because some of these are equivalent (A → B → C and C → B → A are equivalent, for example, because they use the same roads, just in reverse). In actuality, there are 3 possibilities.

This is a function of a mathematical operation called a . Basically:

So the BigOh of the Traveling Salesman problem is or .

Something to think about.

Another point I wanted to make a quick mention of is that any algorithm that has a complexity of is said to have or is solvable in . O(n), O(n) etc. are all polynomial time. Some problems cannot be solved in polynomial time. Certain things are used in the world because of this. Public Key Cryptography is a prime example. It is computationally hard to find two prime factors of a very large number. If it wasn't, we couldn't use the public key systems we use. Anyway, that's it for my (hopefully plain English) explanation of BigOh (revised).

The answer is correct and provides a clear and detailed explanation of Big O notation in plain English, as requested. It covers the worst-case scenario, overall trend, and common Big O notations. The answer is easy to understand and directly addresses the user's question, making it a high-quality response.

Here's a plain English explanation of "Big O" notation:

• Big O is a way to describe how long an algorithm takes to run or how much memory it uses as the input size grows.

• It focuses on the worst-case scenario and the overall trend, not exact numbers.

• Common Big O notations from fastest/most efficient to slowest/least efficient:

- O(1): Constant time - always takes the same amount of time regardless of input size
- O(log n): Logarithmic time - gets a little slower as input grows
- O(n): Linear time - time increases directly with input size
- O(n log n): Linearithmic time - a bit worse than linear
- O(n^2): Quadratic time - gets much slower as input grows
- O(2^n): Exponential time - becomes very slow very quickly

• The goal is usually to find algorithms with lower Big O complexity for better performance.

• It helps compare algorithms and estimate how they'll perform with large amounts of data.

The answer is high-quality, relevant, and provides a clear and concise explanation of Big O notation. It fully meets the user's requirements and provides practical examples to illustrate the concept.

Big O notation is a way to express the efficiency of an algorithm in the worst-case scenario. It describes the upper bound of the time complexity of an algorithm, often using simple mathematical terms.

In plain English, Big O helps us understand how the running time of an algorithm grows as the input size increases. It gives an idea of how efficient an algorithm is and how it might perform with larger data sets.

For example, if we say an algorithm has a time complexity of O(n), it means that the running time grows linearly with the input size. If the input size doubles, the running time also doubles.

Some common Big O notations and their meanings:

- O(1): Constant time complexity. The running time remains constant regardless of the input size.
- O(log n): Logarithmic time complexity. The running time increases logarithmically with the input size.
- O(n): Linear time complexity. The running time increases linearly with the input size.
- O(n^2): Quadratic time complexity. The running time is proportional to the square of the input size.
- O(2^n): Exponential time complexity. The running time doubles with each additional input.

So, in simple terms, Big O notation gives an estimate of how an algorithm's performance will behave as the input grows, helping us compare and choose efficient algorithms.

The answer is correct and provides a clear and detailed explanation of Big O notation. It uses plain English and avoids unnecessary formal definitions and mathematics, as requested by the user. The use of examples and key points helps to clarify the concept and make it more understandable. The answer is easy to follow and provides a good balance between brevity and detail.

Sure. A plain English explanation of the Big O notation is:

**Big O Notation:**

**What it is:**

- It tells us how something grows or decreases compared to other things as it increases.
- It's like a "big picture" comparison of growth rates.

**Think of it like:**

- Imagine you have two functions, one that grows much faster than the other.
- The Big O notation tells you that one function has a growth rate that is
**at least as fast**as the other.

**How it's used:**

- We put a number next to a Big O notation to indicate its growth rate.
- For example, "O(n)" means that the growth rate is
**O(n)**, which means it grows at a rate not slower than n.

**Example:**

- If function A has O(n²) growth rate, meaning it grows
**n squared**times faster than function B, then function A is**O(n²)**.

**Key points:**

- Big O notation tells you the
**upper bound**of the growth rate. - It doesn't tell you the exact growth rate (it could be slower or faster).
- It helps you compare different functions based on their growth rates.

**In summary:**

Big O notation tells you how something grows or decreases compared to other things, based on its **growth rate**. It's a useful tool for understanding and comparing the growth rates of functions.

The answer is correct and provides a clear explanation of Big O notation, using plain English and relatable examples. It covers the most common time complexities and gives a good understanding of how the time taken by an algorithm increases with the amount of data. The answer is easy to read and well-structured, making it a valuable resource for someone looking for a plain English explanation of Big O notation.

**Big O notation** is like a way to describe how fast or slow a task (like a computer program) gets as the amount of work it has to do increases. Here's a simple breakdown:

**O(1) - Constant Time**: No matter how much data you have, the task takes the same amount of time. For example, looking up a value in a hash table.*Example*:`print("Hello, World!")`

- Always takes the same time, no matter how much data you have.

**O(log n) - Logarithmic Time**: The time taken increases slowly as the amount of data increases. For example, searching for a value in a balanced binary search tree.*Example*: A book index - You can find a topic quickly because the index is sorted.

**O(n) - Linear Time**: The time taken increases directly with the amount of data. For example, searching for a value in an unsorted list.*Example*: Finding a specific book in a library by looking at each book one by one.

**O(n^2) - Quadratic Time**: The time taken increases quadratically with the amount of data. For example, bubble sort algorithm.*Example*: Sorting a list of books by comparing each book with every other book.

**O(2^n) - Exponential Time**: The time taken doubles with each addition of data. For example, generating all subsets of a set.*Example*: Trying all possible combinations of a puzzle - The time taken grows very quickly as the puzzle gets bigger.

The answer provides a clear and concise explanation of Big Oh notation in plain English. It also explains the concept of best case, worst case, and average case scenarios.

Quick note, my answer is almost certainly confusing Big Oh notation (which is an upper bound) with Big Theta notation "Θ" (which is a two-side bound). But in my experience, this is actually typical of discussions in non-academic settings. Apologies for any confusion caused.

BigOh complexity can be visualized with this graph: The simplest definition I can give for Big Oh notation is this:

There are some important and deliberately chosen words in that sentence:

Come back and reread the above when you've read the rest. The best example of BigOh I can think of is doing arithmetic. Take two numbers (123456 and 789012). The basic arithmetic operations we learned in school were:

Each of these is an operation or a problem. A method of solving these is called an . The addition is the simplest. You line the numbers up (to the right) and add the digits in a column writing the last number of that addition in the result. The 'tens' part of that number is carried over to the next column. Let's assume that the addition of these numbers is the most expensive operation in this algorithm. It stands to reason that to add these two numbers together we have to add together 6 digits (and possibly carry a 7th). If we add two 100 digit numbers together we have to do 100 additions. If we add 10,000 digit numbers we have to do 10,000 additions. See the pattern? The (being the number of operations) is directly proportional to the number of digits in the larger number. We call this or . Subtraction is similar (except you may need to borrow instead of carry). Multiplication is different. You line the numbers up, take the first digit in the bottom number and multiply it in turn against each digit in the top number and so on through each digit. So to multiply our two 6 digit numbers we must do 36 multiplications. We may need to do as many as 10 or 11 column adds to get the end result too. If we have two 100-digit numbers we need to do 10,000 multiplications and 200 adds. For two one million digit numbers we need to do one trillion (10) multiplications and two million adds. As the algorithm scales with n-, this is or . This is a good time to introduce another important concept:

The astute may have realized that we could express the number of operations as: n + 2n. But as you saw from our example with two numbers of a million digits apiece, the second term (2n) becomes insignificant (accounting for 0.0002% of the total operations by that stage). One can notice that we've assumed the worst case scenario here. While multiplying 6 digit numbers, if one of them has 4 digits and the other one has 6 digits, then we only have 24 multiplications. Still, we calculate the worst case scenario for that 'n', i.e when both are 6 digit numbers. Hence Big Oh notation is about the Worst-case scenario of an algorithm.

The next best example I can think of is the telephone book, normally called the White Pages or similar but it varies from country to country. But I'm talking about the one that lists people by surname and then initials or first name, possibly address and then telephone numbers. Now if you were instructing a computer to look up the phone number for "John Smith" in a telephone book that contains 1,000,000 names, what would you do? Ignoring the fact that you could guess how far in the S's started (let's assume you can't), what would you do? A typical implementation might be to open up to the middle, take the 500,000 and compare it to "Smith". If it happens to be "Smith, John", we just got really lucky. Far more likely is that "John Smith" will be before or after that name. If it's after we then divide the last half of the phone book in half and repeat. If it's before then we divide the first half of the phone book in half and repeat. And so on. This is called a and is used every day in programming whether you realize it or not. So if you want to find a name in a phone book of a million names you can actually find any name by doing this at most 20 times. In comparing search algorithms we decide that this comparison is our 'n'.

That is staggeringly good, isn't it? In BigOh terms this is or . Now the logarithm in question could be ln (base e), log, log or some other base. It doesn't matter it's still O(log n) just like O(2n) and O(100n) are still both O(n). It's worthwhile at this point to explain that BigOh can be used to determine three cases with an algorithm:

Normally we don't care about the best case. We're interested in the expected and worst case. Sometimes one or the other of these will be more important. Back to the telephone book. What if you have a phone number and want to find a name? The police have a reverse phone book but such look-ups are denied to the general public. Or are they? Technically you can reverse look-up a number in an ordinary phone book. How? You start at the first name and compare the number. If it's a match, great, if not, you move on to the next. You have to do it this way because the phone book is (by phone number anyway). So to find a name given the phone number (reverse lookup):

This is quite a famous problem in computer science and deserves a mention. In this problem, you have N towns. Each of those towns is linked to 1 or more other towns by a road of a certain distance. The Traveling Salesman problem is to find the shortest tour that visits every town. Sounds simple? Think again. If you have 3 towns A, B, and C with roads between all pairs then you could go:

Well, actually there's less than that because some of these are equivalent (A → B → C and C → B → A are equivalent, for example, because they use the same roads, just in reverse). In actuality, there are 3 possibilities.

This is a function of a mathematical operation called a . Basically:

So the BigOh of the Traveling Salesman problem is or .

Something to think about.

Another point I wanted to make a quick mention of is that any algorithm that has a complexity of is said to have or is solvable in . O(n), O(n) etc. are all polynomial time. Some problems cannot be solved in polynomial time. Certain things are used in the world because of this. Public Key Cryptography is a prime example. It is computationally hard to find two prime factors of a very large number. If it wasn't, we couldn't use the public key systems we use. Anyway, that's it for my (hopefully plain English) explanation of BigOh (revised).

The answer provides a clear and detailed explanation of Big O notation in plain English, using relatable examples for each complexity class. It directly addresses the user's question and includes all relevant information.

Big O notation is a way to describe the efficiency of an algorithm in terms of its worst-case scenario as the size of the input data increases. It's a high-level measure of how long an algorithm will take to complete or how much memory it will need. Here's a plain English explanation:

**O(1)**- Constant Time: No matter how much data you have, the algorithm takes the same amount of time. It's like having a magic bookmark that lets you jump to the page you want in a book instantly, no matter how many pages there are.**O(log n)**- Logarithmic Time: The time to complete the algorithm grows logarithmically in relation to the size of the input data. Imagine you're looking for a word in a dictionary. If you start in the middle, you can halve the number of words you need to look through with each guess. This is much faster than checking every word one by one.**O(n)**- Linear Time: The time to complete the algorithm grows linearly with the size of the input data. If you have to check every item in a list to find what you're looking for, and the time it takes to check each item is constant, then doubling the size of the list will double the time it takes.**O(n log n)**- Linearithmic Time: This is a combination of linear and logarithmic growth. It's common in efficient sorting algorithms. If you had to organize a pile of papers by importance, and each time you could quickly divide them into two piles but then had to go through each pile to sort them, that would be similar to this complexity.**O(n^2)**- Quadratic Time: The time to complete the algorithm grows quadratically with the size of the input data. If you have to compare each item in a list to every other item, the number of comparisons grows rapidly as the list gets bigger. It's like having a separate conversation with every single person in a room to find out who knows each other.**O(2^n)**- Exponential Time: The time to complete the algorithm doubles with each additional element in the input data. This is like the classic "rice on a chessboard" problem, where each square on a chessboard doubles the amount of rice from the previous square, leading to an astronomical number by the last square.**O(n!)**- Factorial Time: The time to complete the algorithm grows factorially with the size of the input data. This is even worse than exponential growth. It's like trying to arrange a deck of cards in every possible order; the number of arrangements is enormous even for a small deck.

In summary, Big O notation gives us a quick way to talk about how algorithms scale. It doesn't tell us exactly how long an algorithm will take or how much memory it will use; it just describes the trend as the input size grows. This is crucial when comparing algorithms or predicting how they will perform with large datasets.

The answer is correct and provides a clear explanation of Big O notation in plain English. It uses a simple example to illustrate the concept and explains how it can be used to compare the efficiency of different algorithms. The answer also covers both time complexity and space usage, which is great.

Sure, I'd be happy to provide a plain English explanation of "Big O" notation.

Big O notation is a way to describe how the running time or space usage of an algorithm grows as the size of the input increases. It's a way to measure the efficiency of an algorithm.

Imagine you have a simple task, like adding up all the numbers in a list. If the list has 10 numbers, it might take your computer a certain amount of time to do the calculation. If the list has 100 numbers, it might take a bit longer. If the list has 1,000 numbers, it might take even longer.

Big O notation helps us understand how the running time of the algorithm changes as the input size changes. For example, if the running time of an algorithm is proportional to the size of the input, we say it has a "linear" time complexity, and we can describe it using the Big O notation O(n), where "n" represents the size of the input.

On the other hand, if the running time of an algorithm is proportional to the square of the size of the input, we say it has a "quadratic" time complexity, and we can describe it using the Big O notation O(n^2).

The key idea is that Big O notation gives us a way to compare the efficiency of different algorithms, even if they have very different running times. It helps us understand how the algorithm will perform as the input size grows, which is important when we're dealing with large amounts of data.

For example, if you have two algorithms that both solve the same problem, but one has a time complexity of O(n) and the other has a time complexity of O(n^2), the first algorithm will be much more efficient for large inputs, because its running time will grow more slowly as the input size increases.

So, in simple terms, Big O notation is a way to describe the efficiency of an algorithm, and it helps us understand how the algorithm will perform as the input size grows.

The answer is correct and provides a clear explanation in plain English, addressing the user's preference for minimal formal definitions and simple mathematics. It covers common Big O notations and their real-life examples, making it easy to understand for someone unfamiliar with the concept.

Big O notation is a way to describe how the running time or space requirements of an algorithm increase as the input size grows. It provides an upper bound on the growth rate of the algorithm.

Here's a simple, plain English explanation of Big O notation without much formal definition or complex mathematics:

Imagine you have a task to complete, like reading a book. The Big O notation describes how the time to finish the book increases as the size of the book grows.

- If the book gets 10 times larger, does it take 10 times longer to read?
- Or does it take twice as long?
- Or perhaps the time doesn't change at all?

Big O notation helps us answer these questions and compare different algorithms.

Here are a few common Big O notations:

O(1) - Constant Time:

- No matter how big the input is, the algorithm always takes the same amount of time.
- Example: Accessing an element in an array by its index.

O(n) - Linear Time:

- The running time increases linearly with the size of the input.
- If the input size doubles, the running time also doubles.
- Example: Searching for an element in an unsorted array.

O(log n) - Logarithmic Time:

- The running time increases by a constant amount for each doubling of the input size.
- Example: Binary search in a sorted array.

O(n^2) - Quadratic Time:

- The running time is proportional to the square of the input size.
- If the input size doubles, the running time increases by four times.
- Example: Nested loops, like in a simple sorting algorithm (e.g., Bubble Sort).

These are just a few examples, and there are other Big O notations as well.

In simple terms, Big O notation helps us understand how an algorithm's performance scales with the input size. It provides a way to compare different algorithms and choose the most efficient one for a given problem.

Remember, Big O notation represents the worst-case scenario or the upper bound of the growth rate. It gives us an idea of how an algorithm will perform in the worst case as the input size becomes very large.

The answer is correct and provides a clear explanation in plain English with minimal formal definitions and mathematics as requested by the user. It uses a relatable example (checking for an item on a grocery list) to illustrate the concept of Big O notation, and it defines common notations such as O(1), O(n), O(n^2), and O(log n).

Sure, I'll try to explain Big O notation in plain English with minimal formal definitions and mathematics.

Big O notation is a way to measure how efficient an algorithm is, specifically how the running time or space requirements for an algorithm increase as the input size gets bigger.

Imagine you have a grocery list with 10 items, and you need to check if a particular item is on the list. You'd have to look at each item one by one until you either find it or reach the end of the list. This is an example of a simple algorithm.

Now, if your grocery list had 100 items instead of 10, it would take longer to check for a particular item because you'd have to look through more items. The running time of the algorithm increases as the input size (the number of items on the list) gets bigger.

Big O notation gives us a way to describe this increase in running time or space requirements. For example, if we say an algorithm has a time complexity of O(n), it means that as the input size (n) increases, the running time of the algorithm increases at the same rate. So if the input size doubles, the running time also doubles.

Here are some common Big O notations and what they mean in plain English:

**O(1)**(constant time): No matter how big the input is, the algorithm will take about the same amount of time. For example, adding two numbers together has a time complexity of O(1) because it takes the same amount of time regardless of how big the numbers are.**O(n)**(linear time): As the input size increases, the running time increases at the same rate. For example, finding an item in an unsorted list has a time complexity of O(n) because, in the worst case, you'd have to look at every item in the list.**O(n^2)**(quadratic time): As the input size increases, the running time increases much faster, proportional to the square of the input size. For example, a simple algorithm to find pairs of numbers in a list that sum to a target value has a time complexity of O(n^2) because it has to check every pair of numbers.**O(log n)**(logarithmic time): As the input size increases, the running time increases very slowly, much slower than linear time. For example, binary search on a sorted list has a time complexity of O(log n) because it eliminates half of the remaining items on each step.

The goal in algorithm design is to have algorithms with lower time and space complexities, like O(1), O(log n), or O(n), because they scale better for larger inputs. Algorithms with higher complexities like O(n^2) or O(n^3) can become very slow or use too much memory for large inputs.

In summary, Big O notation is a way to describe how the running time or space requirements of an algorithm increase as the input size gets bigger, without getting too bogged down in the specific calculations or constants involved.

The answer provided is correct and gives a good explanation of Big O notation. It uses clear language and provides relevant examples to help illustrate the concept. The explanation of how the run time grows relative to the input as the size of the input increases is particularly well done.

Big O Notation is a theoretical concept in computer science, used to describe the performance or complexity of an algorithm. It's about how the run time grows relative to the input as the size of the input increases.

In simpler terms, Big O notation describes the worst-case scenario, or how slowly running your algorithm would be with a large amount of data.

For instance, if you have an algorithm that goes through every element in a list (like going through it twice to find a specific item), this is considered linear time complexity and we represent it as O(n) because the run time grows linearly with the number of inputs n.

Another example: if there's one operation at each step, no matter how large your data set gets, that would be constant time, denoted by an uppercase letter O like O(1). This happens very fast regardless of size as it always takes a fixed amount of steps (in this case just one step).

So basically Big O notation describes the most dominant part of any function in relation to the input data size. It helps us compare different algorithm’s efficiency and spot where we might want to improve performance.

The answer provides a clear and concise explanation of Big O notation, and the use of examples helps to illustrate the concept. However, it could be improved by providing a more detailed explanation of how Big O notation is used to describe the time complexity of an algorithm.

Imagine you have a list of names and need to find a specific one.

**Scenario 1:**You check each name one by one until you find it. The more names you have, the longer it takes. This is like O(n) - the time it takes grows directly with the number of names.**Scenario 2:**The names are in alphabetical order. You can start in the middle, see if your name is before or after, and keep dividing the list in half. This is much faster, even with a huge list. This is like O(log n) - the time it takes grows much slower than the number of names.

Big O describes how the time (or space used) by an algorithm changes as the input size grows. It focuses on the "big picture" growth, not exact time measurements.

The answer is correct, clear, and concise, providing a good explanation of Big O notation. It includes a plain English explanation, the purpose, common types, and why it matters. The answer is well-structured and easy to understand.

**Big O Notation**: A way to describe how the time (or space) requirements of an algorithm grow as the size of the input increases.**Purpose**: Helps to compare the efficiency of different algorithms, especially for large inputs.**Common Types**:**O(1)**: Constant time - the algorithm takes the same amount of time regardless of input size.**O(log n)**: Logarithmic time - time grows slowly as input size increases (e.g., binary search).**O(n)**: Linear time - time grows directly with input size (e.g., looping through an array).**O(n log n)**: Linearithmic time - common in efficient sorting algorithms (e.g., mergesort).**O(n^2)**: Quadratic time - time grows dramatically with input size (e.g., nested loops).**O(2^n)**: Exponential time - time doubles with each additional input element (e.g., some recursive algorithms).

**Why It Matters**: Understanding Big O helps you choose the right algorithm based on how it will perform as the amount of data increases.

The answer provided is correct and gives a clear explanation of Big O notation using simple terms and examples. It directly addresses the user's request for a plain English explanation with minimal formal definitions and mathematics.

Big O notation is a way to describe how the time or space needed for your program grows as the amount of data it handles increases. Think of it like this:

**Cooking Analogy**: If you're cooking for 4 people, it might take you an hour. But if you suddenly need to cook for 8 people, you might need two hours. In this case, the time it takes to cook is directly proportional to the number of people you're cooking for. We'd say this is O(n) time complexity, where 'n' is the number of people.**Simple Terms**: Big O gives you a way to measure the efficiency of your algorithm when the input size becomes very large. It’s like telling you how busy your program will get as the amount of work increases.**Examples**:**O(1)**: No matter how much data there is, the algorithm takes the same time to complete. It’s like answering a yes/no question based on seeing just one piece of data.**O(n)**: The time or space it takes grows linearly with the amount of data. If you have twice as much data, it takes twice as long.**O(n²)**: If you double the data, the time or space it takes quadruples. This often happens with algorithms that compare each piece of data to every other piece of data.

**Why It Matters**: Knowing the Big O notation helps you choose the right algorithm that performs well as the size of your input grows, ensuring your program runs efficiently even with large amounts of data.

The answer is correct and provides a clear and detailed explanation of Big O notation in plain English. It uses relatable examples and covers common Big O notations. However, it could be improved by providing a more concise explanation upfront and then expanding on the examples and notations. The GitHub repository and Stack Overflow activity sections are not directly related to the answer and could be removed to make the answer more focused.

**Solving the Problem:**

- To explain "Big O" notation in plain English, think of it like this:
- Imagine you're on a road trip and you want to know how long it takes to get to your destination.
- The "Big O" notation measures how long your algorithm takes to complete, relative to the size of the input.

- Here's a simple example:
- Imagine you have a list of 10 items, and you want to find a specific item.
- If you have to check each item one by one, it takes 10 checks.
- But if you have a list of 100 items, it takes 100 checks.
- That's a big difference!

- The "Big O" notation helps us understand how the time it takes to complete an algorithm grows as the input size increases.
- In simple terms, Big O is a way to describe how fast an algorithm gets slower as the input gets bigger.
- Here are some common Big O notations and what they mean:
- O(1) - constant time, like looking up a phone number in a book
- O(log n) - logarithmic time, like finding a word in a dictionary
- O(n) - linear time, like checking each item in a list
- O(n log n) - linearithmic time, like sorting a list of items
- O(n^2) - quadratic time, like comparing each item in a list to every other item
- O(2^n) - exponential time, like trying every possible combination of a puzzle

**Step-by-Step Solution:**

- Understand that Big O notation measures how long an algorithm takes to complete relative to the size of the input.
- Think of it like a road trip and how long it takes to get to your destination.
- Use simple examples to illustrate how Big O notation works, like checking items in a list.
- Understand the common Big O notations and what they mean:
- O(1) - constant time
- O(log n) - logarithmic time
- O(n) - linear time
- O(n log n) - linearithmic time
- O(n^2) - quadratic time
- O(2^n) - exponential time

**GitHub Repository:**

- You can find more information and examples of Big O notation in the "algorithm" repository on GitHub, which has over 1.5 million stars and is one of the most popular repositories on the platform.

**Stack Overflow Activity:**

- The "algorithm" tag on Stack Overflow has over 2.5 million questions and answers, with many of them related to Big O notation and time complexity.
- You can search for questions and answers related to Big O notation to learn more and get help with your specific questions.

The answer is correct, clear, and concise, providing a good explanation of Big O notation and its relevance to algorithmic complexity. It uses relatable examples to illustrate the concept, and it directly addresses the user's preference for a plain English explanation. The only reason it does not receive a perfect score is that it could benefit from a slightly more engaging tone to make it even more accessible to non-experts.

Big O notation is a way to describe the performance or complexity of an algorithm, particularly in relation to the input size. It gives us an idea of how the running time or space requirements grow as the input gets larger.

Think of it this way: Imagine you're comparing different algorithms for performing the same task, like sorting a list of numbers. You might notice that one algorithm seems faster than another when dealing with small lists, but the difference becomes much more pronounced with larger ones. Big O notation helps us quantify and understand this behavior.

For instance, an algorithm with a performance of "O(n)" (Big O of n) would mean its running time grows linearly with the input size: if you double the input, it takes about twice as long to run; if you triple the input, it'll take around three times as long, and so on. This is considered relatively simple and efficient, like Bubble Sort or Insertion Sort algorithms.

On the other hand, an algorithm with a performance of "O(n^2)" (Big O squared of n) would mean its running time grows much faster than linearly: if you double the input, it might take 4, 9, or even 16 times longer to run. This is considered less efficient for larger inputs, like Bubble Sort when sorting large lists.

Big O notation abstracts these complexities by focusing on the most dominant term in the algorithm's complexity. By comparing different algorithms using Big O notation, you can easily determine which one scales better with increasing input size and thus is more likely to be a good choice for larger data sets or more demanding tasks.

The answer provided is correct and gives a clear explanation of Big O notation. It uses simple language and avoids unnecessary formal definitions, which aligns with the user's request. However, it could benefit from some improvements in terms of making the explanation more accessible to someone who might not be familiar with programming concepts such as loops or arrays.

"Big O" notation is used to measure how the amount of work done by an algorithm grows as its input size increases. It can be thought of like a benchmark for measuring the growth of an algorithms' run-time. This helps developers optimize their algorithms and write more efficient code. The "O" refers to a specific method or technique known as the order of the notation. The amount of work done by an algorithm is measured in terms of the size of its input, so if an algorithm has a time complexity of O(n), where n is the size of the input, the run time will grow with the size of the input. This means that if we double the size of our input, the runtime will double. For instance, a for-each loop with a linear search might have a big O of O(n^2), which means that as the length of an array grows, so does the number of steps taken to find a certain value inside of it.

The answer is correct and provides a clear explanation of Big O notation with examples. It could benefit from a brief introduction and assuming less prior knowledge about algorithm complexity.

Big O notation is a way to describe how fast an algorithm's runtime grows relative to the size of its input. It helps us understand the worst-case scenario for how long an algorithm might take to run as the input gets larger.

**O(1)**: Constant time. The algorithm takes the same amount of time no matter how big the input is.**O(log n)**: Logarithmic time. The algorithm's runtime grows slowly as the input size increases. Common in algorithms that divide the problem in half repeatedly.**O(n)**: Linear time. The algorithm's runtime grows directly proportional to the size of the input.**O(n log n)**: Linearithmic time. Faster than O(n^2) but slower than O(n). Common in efficient sorting algorithms.**O(n^2)**: Quadratic time. The algorithm's runtime grows exponentially (squared) with the size of the input.**O(2^n)**: Exponential time. The algorithm's runtime doubles with each addition to the input size.**O(n!)**: Factorial time. The algorithm's runtime grows extremely fast, factorial-wise, with the size of the input.

These notations help us compare and choose algorithms based on their efficiency for different sizes of input.

The answer provides a good analogy for Big O notation and explains how time complexity grows with input size. However, it could be improved by explicitly defining 'n' and providing an example with larger input values.

Imagine you're trying to find a specific book in a library.

**O(1)**: You know exactly where the book is, like it's on the top shelf. You can grab it instantly.**O(n)**: You have to search through all the books on one shelf. The time it takes depends on how many books are on that shelf (n).**O(n^2)**: You have to search through all the shelves, and for each shelf, you have to search through all the books on that shelf. The time it takes depends on how many shelves (n) and how many books on each shelf (n).

**Big O notation** describes how the time it takes to solve a problem grows as the size of the problem grows. It's like a way to measure how "slow" an algorithm is.

The answer provided is correct and gives a clear explanation of Big O notation in plain English with simple mathematics. The example given at the end helps illustrate the concept further. However, it could be improved by providing more examples or explaining how to calculate Big O for different types of functions.

Sure, here is a plain English explanation of "Big O" notation with simple mathematics:

**Big O notation** describes the upper bound of a function's growth rate in comparison to another function. It's like saying how much the function will increase as its input grows infinitely.

Imagine you have two functions, f(x) and g(x). Big O notation tells you how much f(x) can be bigger than g(x) as x gets larger and larger.

Here's the math:

**Big O(n) = f(n) is O(g(n)) if lim (n→∞) [f(n)/g(n)] = c for some constant c > 0**

What this means is:

**Big O(n)**is like a ceiling function that bounds the upper limit of f(n).**g(n)**is the function that defines the upper bound.**c**is a constant multiplier that can be adjusted to fit the specific function.

For example, the function f(x) = 2x + 1 has a Big O notation of O(x) because the growth rate of f(x) is bounded above by the growth rate of x.

**So, Big O notation is a way to describe the upper bound of a function's growth rate in simple terms. It helps programmers compare the efficiency of different algorithms and data structures.**

The answer provided is correct and gives a clear explanation of Big O notation in plain English. It uses relatable examples to explain how the runtime grows with the size of the input. The answer could have been improved by providing more specific examples or use cases.

Big O notation is a way to describe how the runtime of an algorithm grows as the input size increases. Here's a plain English explanation:

- Big O notation helps us understand how an algorithm's performance scales based on the size of the input.
- It's like describing how long it takes for an algorithm to run as the amount of data it needs to process grows.
- For example, if an algorithm has a Big O notation of O(N), it means that the runtime grows linearly with the size of the input.
- If an algorithm has a Big O notation of O(N^2), it means that the runtime grows quadratically with the size of the input.
- The lower the order of the Big O notation, the faster the algorithm is considered to be.

The answer provided is essentially correct and gives a good explanation using an analogy that is easy to understand. However, it could benefit from some minor improvements such as providing more concrete examples or explaining the concept of 'n' in more detail.

Here's a plain English explanation of "Big O" notation:

Big O notation is a way to measure how long an algorithm takes to complete. It's like a speed limit for your code.

Imagine you have a box of toys, and you need to find a specific toy. Here are a few ways to do it:

**O(1)**: You know exactly where the toy is, so you can grab it immediately. This is like a constant time complexity.**O(log n)**: You have a map of the box, so you can find the toy by dividing the box in half, then in half again, and so on. This is like a logarithmic time complexity.**O(n)**: You have to look through the box one toy at a time until you find the one you want. This is like a linear time complexity.**O(n log n)**: You have to look through the box, but you also have to sort the toys as you go. This is like a linearithmic time complexity.**O(n^2)**: You have to look through the box, and for each toy, you have to look through the box again. This is like a quadratic time complexity.**O(2^n)**: You have to look through the box, and for each toy, you have to look through the box again, and again, and again. This is like an exponential time complexity.

The bigger the O, the longer the algorithm takes to complete. So, O(1) is the fastest, and O(2^n) is the slowest.

That's Big O notation in a nutshell!

The answer is generally correct and provides a good analogy to explain Big O notation. However, it could be improved by explicitly stating that Big O notation measures the upper bound of an algorithm's time complexity and that constants and lower order terms are ignored. The answer should also clarify that the Big O notation in this example should be O(n) instead of O(6), where n represents the number of batches.

Imagine you're baking cookies. You have a recipe that takes 2 minutes to mix the dough, then 3 minutes to bake them in the oven, and finally 1 minute to let them cool.

The total time it takes to make one batch of cookies is: 2 + 3 + 1 = 6 minutes

Now, imagine you want to make 10 batches of cookies. The recipe doesn't change, but the total time it takes will be longer because you're making more batches. Let's say each batch still takes 2 minutes to mix, 3 minutes to bake, and 1 minute to cool.

The total time it takes to make 10 batches is: 6 minutes per batch × 10 batches = 60 minutes

This is like the "Big O" notation in computer science. It measures how long an algorithm (like a recipe) takes to complete, but it's simplified to just look at the longest part of the process.

In this example, the Big O would be O(6), because that's the longest time it takes to make one batch. If you were making 100 batches, the total time would still be around 600 minutes (10 times longer than before). The Big O wouldn't change, because the algorithm is still just mixing, baking, and cooling - it's not getting more complicated.

So, in simple terms: Big O measures how long an algorithm takes to complete by looking at its longest part.

The answer is correct and provides a clear explanation with examples. The response is easy to understand and covers the main points of Big O notation. However, there is room for improvement in terms of brevity, as the answer could be more concise while still maintaining its clarity.

Big O notation is a way to express the time complexity or space complexity of an algorithm, describing how the running time or space requirements grow as the input size increases.

In simple terms, Big O notation describes the upper bound of the time complexity in the worst-case scenario. It measures the scaling factor of an algorithm when the input size increases.

For example, if we have an algorithm with time complexity O(n), it means that if we double the input size, the time it takes to run the algorithm will also approximately double. If an algorithm has time complexity O(n^2), increasing the input size will have a more significant impact on the time it takes to run the algorithm.

Let's look at some common Big O notations and their real-world algorithm examples to better understand this concept:

- O(1): Constant time complexity. No matter how big the input size is, the time taken to complete the operation remains constant. Example: Accessing an array element by its index.

```
def get_element(arr, index):
return arr[index] # Constant time complexity O(1)
```

- O(log n): Logarithmic time complexity. The time taken to complete the operation increases logarithmically with the input size. Example: Binary search.

```
def binary_search(arr, target):
low, high = 0, len(arr) - 1
while low <= high:
mid = (low + high) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
low = mid + 1
else:
high = mid - 1
return -1 # Logarithmic time complexity O(log n)
```

- O(n): Linear time complexity. The time taken to complete the operation increases linearly with the input size. Example: Finding an element in an unsorted list or array.

```
def linear_search(arr, target):
for i in range(len(arr)):
if arr[i] == target:
return i
return -1 # Linear time complexity O(n)
```

O(n log n): Linearithmic time complexity. The time taken to complete the operation increases linearly and logarithmically with the input size. Example: Quick sort or merge sort.

O(n^2): Quadratic time complexity. The time taken to complete the operation increases with the square of the input size. Example: Bubble sort, selection sort, or insertion sort.

```
def bubble_sort(arr):
n = len(arr)
for i in range(n):
for j in range(0, n - i - 1):
if arr[j] > arr[j + 1]:
arr[j], arr[j + 1] = arr[j + 1], arr[j] # Quadratic time complexity O(n^2)
```

These are some of the most common Big O notations you will encounter. By understanding them, you can analyze and compare algorithms to determine their efficiency in terms of time and space complexity.

The answer is correct and provides a good explanation of Big O notation, including examples of O(n) and O(1). It also mentions the use of Big O notation to compare algorithms based on their worst-case scenarios. However, it could benefit from a simpler explanation of the concept, using even more plain English and avoiding technical terms like 'runtime' and 'space usage'.

- Big O notation describes how the runtime or space usage grows with input size.
- It helps compare algorithms by focusing on their worst-case scenarios.
- Example: "O(n)" means time increases linearly with input size, like adding numbers in a list.
- "O(1)" indicates constant time, regardless of input size (e.g., accessing an array element).
- Use it to choose the most efficient algorithm for your problem's scale and data.

The answer provided is correct and gives a good explanation of Big O notation in plain English. However, it could be improved by providing an example or two to help illustrate the concept more concretely.

Sure, I can provide an explanation of Big O notation in plain English. Big O notation is a way to express how the performance of a function depends on its input size. It is used to compare different algorithms or to analyze the complexity of a specific operation. In plain English, Big O notation is a measure of how fast a function runs as you increase its input size. It gives an idea of which algorithm or operation would be best suited for a given task.

The answer is essentially correct and provides a good explanation of Big O notation, including examples of different time complexities. However, it could benefit from a brief introduction to the context and importance of Big O notation in computer science. The answer is relevant and helpful, but it lacks a personal touch that would make it more engaging for the user.

- Big O notation describes the performance or complexity of an algorithm
- It shows the worst-case scenario and the execution time required or the storage space used
- Uses simple math functions to represent the relationship between input size and operations needed
- O(1) represents constant time, operations take the same time regardless of input size
- O(n) represents linear time, operations increase linearly with input size
- O(n^2) represents quadratic time, operations increase exponentially with input size
- O(log n) represents logarithmic time, operations increase logarithmically with input size
- O(2^n) represents exponential time, operations double with each addition to the input size

The answer is generally correct and provides a high-level explanation of Big O notation. However, it could benefit from a more specific explanation of how Big O notation describes the performance of an algorithm, such as by using examples of different Big O complexities (e.g. O(1), O(n), O(n^2), etc.).

Big O notation is a way to describe the performance of an algorithm. It tells you how long an algorithm will take to run, in layman's terms. It's like a roadmap that shows how the algorithm's runtime grows and scales with the size of the problem it's trying to solve.

The answer is generally correct and provides a good example in plain English. However, it could be improved by directly addressing the 'time complexity' aspect of Big O notation, as mentioned in the question's tags. The cake recipe example does not explicitly connect to time complexity until the end.

**What is "Big O" Notation?**

Imagine you have a recipe for a cake. The recipe tells you that it takes 2 minutes to mix the batter and 1 minute to bake it. Now, suppose you want to make multiple cakes. How long will it take to make N cakes?

Here's where "Big O" notation comes in. It's a way to talk about how long an algorithm (a recipe for solving a problem) will take as the input size (the number of cakes) gets larger and larger.

**The "O" in "Big O"**

The "O" in "Big O" notation stands for "order of growth." It's like the pattern of how the time to complete a task increases as the input size increases.

**Plain English Explanation:**

For our cake recipe, the time it takes to make N cakes is 2 minutes (mixing batter) + 1 minute (baking) * N. As N gets larger, the time it takes to bake the cakes (1 minute * N) grows much faster than the time it takes to mix the batter (2 minutes).

We say that the time complexity of our cake recipe is O(N), which means that the time it takes to make N cakes grows in the same order as N.

**Key Points:**

- "Big O" notation is a way to describe how long an algorithm takes as the input size gets larger.
- The "O" stands for "order of growth."
- The time complexity of our cake recipe is O(N), which means that the time it takes to make N cakes grows in the same order as N.

The answer is mostly correct and provides a good explanation of different Big O notations, but it lacks a plain English explanation of what Big O notation is. The answer jumps straight into examples without explaining the concept first.

Big O notation tells you roughly how much longer an algorithm will take to run as you give it more data.

**O(1)**- Always takes the same time to run, regardless off the data size.**O(n)**- Time to run increases linearly with the data size.**O(log n)**- Time to run increases logarithmically with the data size.**O(n^2)**- Time to run increases quadratically with the data size.**O(2^n)**- Time to run doubles for each additional element in the data set.