Time Complexity is important in the programming world to evaluate the optimal way to perform specific operations. There are various approaches to problem-solving in computer programming, just like there are in other facets of life. We need to assess the effectiveness of several strategies in order to choose the best one because these diverse techniques could indicate different times, computational power, or any other metric you choose.
As you may already be aware, computers can solve issues using algorithms.
An algorithm is a series of steps or a procedure that tells a computer what to do and how to execute it.
They have changed so much in modern times that they can perform the same duty quite differently. In the worst scenario (which is really rather often), several algorithms implemented in various programming languages may instruct various computers with various hardware and operating systems to do the same work in various ways. What a weird idea, right?
The problem is that even with modest data sets, one technique may take seconds to complete while another may take minutes. How can we evaluate several performances and select the top method to address a specific issue?
Fortunately, there are methods for achieving this, and we don’t need to watch the algorithm in action to determine whether it can complete the task fast or if its input will cause it to break down. When evaluating an algorithm’s complexity, we shouldn’t actually be concerned with the precise number of operations carried out; rather, we should be concerned with how the number of operations relates to the size of the problem. Do the number of operations remain the same if the problem size doubles? Do they multiply? Do they also grow in other ways? We must evaluate the temporal complexity of algorithms to provide answers to these problems.
Time complexity represents the number of times a statement is executed. The actual time needed to run a piece of code is not measured by an algorithm’s time complexity because that depends on other elements like the operating system, programming language, and processor speed, among others. The concept behind time complexity is that it can only gauge an algorithm’s execution time in a way that solely depends on the method and its input.
We employ a technique known as “Big O notation” to describe how time-consuming a method is. We employ a language called the Big O notation to express how time-consuming an algorithm is. It’s how we assess the effectiveness of several solutions to an issue and aids in our decision-making.
Big O notation describes an algorithm’s run time in terms of how quickly the input (referred to as “n”) expands in comparison to the algorithm. Thus, if we were to assert, for instance, that an algorithm’s runtime increases “on the order of the size of the input,” we would write it as “O(n)”. If we want to state that an algorithm’s execution time increases “on the order of the square of the size of the input,” we would use the notation “O(n2)”. But exactly what does that mean?
Knowing the speeds at which things might increase is essential to comprehending time complexity. Time taken per input size is the unit of measurement here. There are various time complexity kinds, so let’s look at the most fundamental ones first.
Constant Time Complexity: O(1)
The size of the input (n) is irrelevant when temporal complexity is constant (noted as “O(1)”). Independent of the size of n, algorithms with constant time complexity run for a constant amount of time. They are the quickest algorithms available since their run-time is constant regardless of the input data.
For instance, if you wanted to determine whether a number is odd or even, you would use an algorithm with constant time complexity. The method would only carry out the same operation once, giving you the output whether the input “n” was 1 or 9 billion.
Additionally, you would run a phrase like the well-known “Hello World” with constant time complexity if you only wanted to print it out once. This is because no matter what operating system or machine configuration you are using, the number of operations (in this case 1, in this phrase) will always be the same.
These algorithms must not include loops, recursions, or calls to any other non-constant time function if they are to remain constant. The run-time for algorithms with constant time does not change; the order of magnitude remains constant at 1.
Linear Time Complexity: O(n)
You are faced with linear time complexity, sometimes known as O(1), when time complexity becomes directly proportionate to the quantity of the input (n). The input (n) will be processed by algorithms with this time complexity in “n” operations. This implies that the method requires proportionally more time to complete as the input increases.
These are the kinds of circumstances where you must examine each item in a list in order to complete a task (e.g. find the maximum or minimum value). You may also consider commonplace activities like reading a book or looking for a CD in a stack of CDs. If all the data must be reviewed, the more operations required, the bigger the input size.
The fact that the algorithm examines every element in the input results in linear running time algorithms, which are quite prevalent.
Logarithmic Time Complexity: O(log n)
This level of complexity allows for amazingly quick computing. When an algorithm’s execution time is inversely correlated with the input size, it is said to execute in logarithmic time. This indicates that the time required to complete each succeeding step decreases at a rate that is inversely proportional to the input “n,” as opposed to growing.
What’s the trick behind it? These algorithms often function by eliminating substantial portions of input that haven’t been inspected with each step, thus they never need to go through all of the input. This time complexity is typically related to algorithms that “divide and conquer” problems by splitting them in half each time. Using the following steps, Divide and Conquer algorithms solve problems:
- They separate the provided problem into smaller issues of the same kind.
They resolve these subproblems recursively. - They combine the sub-answers in the right way to resolve the given issue.
Think about this scenario: Suppose you wish to find a term in a dictionary where the words are all arranged alphabetically. To do that, there are at least two algorithms:
Algorithm A:
- Begins from the start of the book and proceeds chronologically until it locates the person you’re looking for.
Algorithm B:
- Check the first word on the page by opening the book in the middle.
- If the word you’re looking for is larger in the alphabet, it will appear in the right side. Other than that, it appears in the left half.
Which of the two is faster? Method B is substantially more effective at attaining the same result than algorithm A, which works word by word in O(n). Algorithm B divides the issue in half on each iteration in O(log n).
After constant time algorithms (O(1)), logarithmic time algorithms (O(log n)) are the fastest.
Quadratic Time Complexity: O(n²)
This sort of method rises in execution time exactly proportionate to the square of input size (like linear, but squared).
Algorithms having quadratic time complexity take a long time to execute in most situations, especially for huge data sets, and should be avoided.
Because you’re performing one linear operation inside another, or n*n, which equals n2, nested for loops run in quadratic time.
If you encounter these kinds of algorithms, you will either require a significant amount of time and money or you will need to develop a better algorithm.
Exponential Time Complexity: O(2^n)
With each addition to the input (n), the growth rate in exponential time algorithms doubles, frequently repeating across all subsets of the input items. You have to do twice as many procedures if an input unit goes up by 1. Right, this doesn’t sound good.
When you need to test every combination or permutation of the data because you don’t know for sure what the optimum answer is, you typically utilise algorithms with this level of time complexity.
Brute-Force algorithms frequently exhibit exponential time complexity. These algorithms run blindly across the whole space of potential solutions in search of one or more that meet the requirement. They simply test every potential answer until they chance to find the right one in an effort to find the best one. Given that it will impact the task’s temporal complexity, this is certainly not the best approach. In cryptography, brute-force algorithms are employed as attacking strategies to get around password protection by attempting random strings until the right password is found that unlocks the system.
You should avoid algorithms with exponential running times since they scale poorly, just like with quadratic time complexity.
How do measure time complexity?
In general, we have shown that the speed of an algorithm increases with the number of operations. How can we put this wonderful principle into practice in the actual world?
How can we determine an algorithm’s temporal complexity if we already have one (whatever it may be)?
In some circumstances, this might be rather simple. Imagine that you have two For loops: an outer one that iterates through every item in the input list, and an inner one that is nested and iterates through every item in the input list once more. N * N, where n is the number of elements in the input array, is the total number of steps that were taken.
But how can you determine a complex function’s time complexity?
We must dissect the algorithm code into its component components in order to determine its difficulty in order to arrive at the solution. I’m sorry to break the news to you, but there isn’t a button you can press to get information on an algorithm’s time complexity. You must complete it on your own.
Although it won’t always be achievable, it is generally preferable to have your functions running below or within the range of linear time complexity.
There are many Big O notations, such as “best case,” “average case,” and “worst case,” but the worst-case scenario is what counts most because that is the one that has the potential to truly crash everything. They get directly to the core of why temporal complexity matters and highlight the fact that some algorithms are unable to complete a task without doing so in a few billion years.
The number of fundamental operations that must be carried out at the most during the algorithm’s execution is determined by the worst-case analysis. It is predicated on the idea that the input is in the worst possible condition and therefore the most effort must be expended to correct it. The worst-case scenario, for a sorting algorithm that wants to arrange an array in ascending order, is when the input array is arranged in descending order. In this scenario, the array must be set in ascending order using the fewest possible fundamental operations (comparisons and assignments). Consider this: if you were to read every name in a directory to find the one you were looking for, the worst-case scenario is that the name you are looking for is the very last item.
In conclusion, an algorithm will do its work more quickly in practice the lower its temporal complexity is. When creating or managing algorithms, you should be aware of this issue because it can significantly affect whether the algorithm is useful or entirely useless.
