## What is the best time complexity to have?

Rank 1: Constant Time Complexity

We should say that the best algorithm would have time complexity of **O(1)** which is a constant. This means the run time of the algorithm is either independent of the data size or there is a constant such that the runtime is bounded above by a constant.

**Which time complexity is best and why?**

O(1) has the least complexity

Often called “constant time”, **if you can create an algorithm to solve the problem in O(1), you are probably at your best**. In some scenarios, the complexity may go beyond O(1), then we can analyze them by finding its O(1/g(n)) counterpart. For example, O(1/n) is more complex than O(1/n²).

**Which is better O 1 or O log N?**

Case: where O(log n) outperforms O(1)

Let us assume hypothetically that a print statement takes 1ms to execute. So for n=2, Code 1 will take 4 ms to execute whereas Code 2 will take just 1 ms to execute. In this case, O(log n) outperformed O(1).

**How do you find the best case time complexity?**

Big-Omega is a notation to represent the best case time complexity of an algorithm. It provides a lower bound for the running time of an algorithm. **Best case time complexity = Ω(rate of growth of the best case running time)**.

**Which time complexity is the fastest?**

The fastest possible running time for any algorithm is **O(1)**, commonly referred to as Constant Running Time. In this case, the algorithm always takes the same amount of time to execute, regardless of the input size. This is the ideal runtime for an algorithm, but it's rarely achievable.

**What is the most common time complexity?**

The most common complexity classes are (in ascending order of complexity): **O(1), O(log n), O(n), O(n log n), O(n²)**. Algorithms with constant, logarithmic, linear, and quasilinear time usually lead to an end in a reasonable time for input sizes up to several billion elements.

**Which time complexity is worst?**

In the case of running time, the **worst-case time complexity** indicates the longest running time performed by an algorithm given any input of size n, and thus guarantees that the algorithm will finish in the indicated period of time.

**Which is better Nlogn or N?**

So for higher values n, n*log(n) becomes greater than n. And that is why **O(nlogn) > O(n)**. No matter how two functions behave on small value of n , they are compared against each other when n is large enough. Theoretically, there is an N such that for each given n > N , then nlogn >= n .

**Which is better time or space complexity?**

Space complexity is usually referred to as the amount of memory consumed by the algorithm. It is composed of two different spaces; Auxiliary space and Input space. **The factor of time is usually more important than that of space**.

**Is Big-O the worst case?**

Big O is also known as the algorithm's upper bound since it analyses the worst-case situation. The best-case scenario usually tells us nothing — we'll possibly solve the problem on the first try.

## Is n logN better than n 2?

The only thing we can say for sure is that **nlogn algorithm outperforms n2 algorithm for sufficiently large n**. In practice, all nlogn algorithms have low enough multipliers that n2 algorithm can be quicker only for very small n (and for very small n, it usually doesn't matter what algorithm is used).

**Is anything faster than O 1?**

There is exactly one such algorithm: an empty one. Performs no operations (does not access input and does not produce output) and runs in a zero time. Other than that, **there are no algorithms with asymptotic computational complexity (measured in operations count) better than O(1)**.

**What is best and worst complexity?**

**Best case is the function which performs the minimum number of steps on input data of n elements.** **Worst case is the function which performs the maximum number of steps on input data of size n**.

**What is best and worst case time complexity?**

**Best Case − Minimum time required for program execution.** **Average Case − Average time required for program execution.** **Worst Case − Maximum time required for program execution**.

**What is best average and worst time complexity?**

**In the simplest terms, for a problem where the input size is n:**

- Best case = fastest time to complete, with optimal inputs chosen. For example, the best case for a sorting algorithm would be data that's already sorted.
- Worst case = slowest time to complete, with pessimal inputs chosen. ...
- Average case = arithmetic mean.

**Can time complexity be less than 1?**

**Not really**. O(1) is constant time.

**What is an example of time complexity?**

Here's an example: As you can see, **the message "Hello World!!" is printed only once**. So, regardless of the operating system or computer configuration you are using, the time complexity is constant: O(1), i.e. any time a constant amount of time is required to execute code.

**Which complexity will take the slowest time?**

**The common algorithmic runtimes from fastest to slowest are:**

- constant: Θ(1)
- logarithmic: Θ(log N)
- linear: Θ(N)
- polynomial: Θ(N^2)
- exponential: Θ(2^N)
- factorial: Θ(N!)

**What is average and worst case time complexity?**

Just as the worst-case complexity describes an upper bound on the worst-case time we would see when running an algorithm, **average case complexity will present an upper bound on the average time we would see when running the program many times on many different inputs**.

**Which sort has best complexity?**

The **quick sort algorithm** has the best time complexity of all the sorting algorithms. The best, average, and worst cases of Quicksort's temporal complexity are O(N log N), O(N log N), and O(N log N), respectively.

## Is log n better than constant time?

**constant time is better than log(n) time in most cases**. In edge cases where log(n) is smaller than the constant it will be faster (in the real world). Remember that one billion and 1 are both "constant" in big O notation.

**What grows faster logn or n?**

1*n is O(n). 10000000000000000000000000000000*(log n) is O(log n). In such a case, O(n) will not just be faster on extremely small input. But as "n" grows toward infinity, eventually **O(log n) will be faster**.

**Why space complexity is not so important?**

In the end, the function we come up with will be independent of the actual number of bytes needed to represent the unit. Space complexity is sometimes ignored **because the space used is minimal and/or obvious**, but sometimes it becomes as important an issue as time.

**Does time complexity matter?**

To sum up, **the better the time complexity of an algorithm is, the faster the algorithm will carry out the work in practice**. You should take into account this matter when designing or managing algorithms, and consider that it can make a big difference as to whether an algorithm is practical or completely useless.

**Why time complexity is more important?**

In constant time complexity, the algorithm will take the same amount of time to run regardless of how large the input size is. It is an essential property because **as long as you have enough memory, you should be able to process any input size reasonably**.

**Is little O stronger than Big O?**

These both describe upper bounds, although somewhat counter-intuitively, **Little-o is the stronger statement**. There is a much larger gap between the growth rates of f and g if f ∈ o(g) than if f ∈ O(g). One illustration of the disparity is this: f ∈ O(f) is true, but f ∈ o(f) is false.

**Why is Big O best case?**

Big O is sometimes referred to as the algorithm's upper bound, meaning that it deals with the worst-case scenario. **The best-case scenario doesn't really tell us anything — it will be finding our item in the first pass**. We use worst-case to remove uncertainty — the algorithm will never perform worse than we expect.

**Is Big O best or worst?**

**Worst case** — represented as Big O Notation or O(n)

Big-O, commonly written as O, is an Asymptotic Notation for the worst case, or ceiling of growth for a given function.

**Is lower time complexity better?**

An algorithm with O(n) time complexity ensures the running time is proportional to the input size and will take more time as we increase the input size. An algorithm's time complexity is measured by calculating how long it takes for the program to finish its work. **The lower, the better**.

**Which time complexity represent the most efficient algorithm?**

Constant Time Complexity: **O(1)**

Algorithms with Constant Time Complexity take a constant amount of time to run, independently of the size of n. They don't change their run-time in response to the input data, which makes them the fastest algorithms out there.