Characteristics of Algorithm in Python

What is an Algorithm?

An algorithm is a set of instructions or rules to complete an action or solve a problem. It is a methodical technique that can be employed to execute a computer program, solve a mathematical puzzle, or perform an action like sorting a list.

Algorithms can categorize items, solve challenging issues, detect patterns in data sets, and solve equations. Additionally, algorithms can be used to perform operations like sorting, searching, or computing.

An algorithm can also specify a set of rules for carrying out a specific action. Algorithms can be created to complete various tasks and challenges, from the simple to the complicated.

Algorithms can be used to automate several activities, such as robot control and email sending. Algorithms are now more critical than ever in the field of computer science.

A particular programming language, like C++, Java, or Python, is typically used to write algorithms. After that, the algorithm is converted into a format that a computer can understand, like the machine code.

They are employed in creating efficient programs and quickly and precisely handling enormous amounts of data. Algorithms are also used in data analysis to look for patterns, predict the future, and connect various data sets.

Let's look at the definitions of a few terms so that we can fully understand an algorithm.

• Problem: It's a situation or a real-world problem that requires rules or instructions to be created to achieve the intended outcome.
• Input: These are the input parameters that an algorithm needs to produce the desired result.
• Output: After supplying the specified input in accordance with the created algorithm, the required output is obtained.

Characteristics of an Algorithm

Let us now look at the characteristics of an algorithm.

Well-defined Inputs

• An algorithm's expected inputs must be clearly stated to ensure accuracy, predictability, and repeatability.
• The behavior of the algorithm is deterministic, which means that the same input will always result in the same output due to well-defined inputs.
• The need for the algorithm is less likely to be misunderstood or implemented incorrectly with the help of unambiguous inputs.
• It is simpler to optimize the algorithm based on the input's properties when the inputs are clearly described.

Well-defined Outputs

• To ensure that an algorithm provides the desired and accurate result for a given set of inputs, the outputs of the method should be clearly stated.
• It keeps the algorithm clear and ensures that the problem is successfully solved.
• The implementation of the algorithm may also be easily checked for accuracy.
• The technique can be further optimized to produce the results more quickly if the outputs are clearly defined.

Unambiguity

• Ambiguity in the description of the algorithm can result in accurate implementations and reliable results. An algorithm must be precise for this reason.
• It enables predictability in the algorithm, i.e., the same input provides the same output, which facilitates implementation debugging.
• Unambiguous is also more easily standardized and widely applied for various applications.
• When implementing precise algorithms, you can concentrate more on optimizations than resolving unexpected failures and edge circumstances.

Finiteness

• The algorithm should have a finite number of instructions and come to a finish at some point.
• A finite algorithm guarantees that it will ultimately run out of resources and produce a result.
• In real-life situations where computing can only be performed for a while, an infinite algorithm is unworkable since it would never conclude.
• In the case of a finite algorithm, the time and space complexity can be examined, which is crucial for carrying out additional optimizations.

Language Independent

• Language-independent algorithm is more versatile and usable in various situations since it is simple to port it to multiple platforms and programming languages.
• A future-proof algorithm can be easily implemented in newer programming languages, hence being language-independent.
• Algorithms must be independent of programming languages in educational environments where students are exposed to them.
• Furthermore, it facilitates the performance evaluation of the algorithm in other programming languages.

Effectiveness and Feasibility

• An algorithm should be feasible, which means it is practical and can be executed within appropriate limits and resources.
• Additionally, it stays away from long execution times, which can render an algorithm useless in practical applications.
• Without the need for specialized resources, feasible algorithms can be quickly implemented by utilizing the hardware infrastructure that is already in place.
• Due to their reasonable hardware requirements, they are quickly embraced for use in various fields.

Types of Algorithms

1. Brute-Force Algorithm

Brute force algorithms are uncomplicated, easy-to-use problem-solving methods that use trial and error.

When no other adequate remedy is known, these are frequently used to solve problems. Even though they can be slow, they are simple to understand and perform.

2. Sorting Algorithm

A data structure is sorted using sorting algorithms in accordance with a set of rules. They are typically used to organize components in ascending or descending order. These include insertion sort, bubble sort, and more.

3. Searching Algorithm

The search algorithm looks for a specific element or collection of items in a data structure. Any search algorithm can be used, including binary search and linear search

4. Backtracking Algorithm

The backtracking algorithm looks for every possible result. To locate the desired answer, we follow each path to its first failure point and then look for a possible solution in the following path. The algorithm requires additional time.

5. Recursive Algorithm

Recursion is a concept that is applied to the recursive algorithm. It divides a problem into smaller problems and repeatedly invokes the same function for each of the smaller problems. It performs this repeatedly until it reaches a base case that ends the function if the predetermined requirements are satisfied.

6. Divide and Conquer

The Divide and Conquer algorithm divides a problem set into smaller subsets, solves each separately, and then combines them for the complete solution of the original problem. Merge sort, quick sort, and others are examples of this algorithm.

7. Greedy Algorithm

Greedy algorithms always seek the best answer and keep doing so until they reach the end result. Although the result of this approach may or may not be ideal, it computes results quite quickly. These comprise several algorithms, such as topological sort, selection sort, and others.

Assume that we must follow the path with the lowest cost from the starting point to the finishing point in the provided graph. It is written on it that indicates how much an edge costs.

If we employ the greedy method, it will initially select the option with a cost of four because it is less expensive than the path with a cost of five. Finally, the solution will output a path costing 13 using a greedy method. But in truth, the 12-cost path is the shortest one.

8. Dynamic Programming Algorithm

Algorithms for dynamic programming are employed to address issues that can be divided into smaller issues. They take a bottom-up strategy, starting with the smallest sub-problems and working up to the most challenging ones.

This method is used to identify the best answer to a problem by minimizing unnecessary calculations.

9. Randomized Algorithm

A randomized algorithm uses random integers to decide what to perform at each given point in its logic. The results for the same inputs on different runs may differ due to the additional randomization. It is frequently used in cryptographic techniques to maintain security. Additionally, it helps in avoiding worst-case circumstances that a deterministic algorithm can encounter. For instance, randomized quick sort performs better than conventional quick sort in the worst-case scenario.

Complexity of Algorithm

An algorithm's characteristics enable it to produce the required outcomes, but an algorithm's performance is based on how complex it is. The two main difficulty types are time complexity and space complexity.

Time Complexity

• The time complexity of an algorithm measures how long it will take to complete, depending on the input size.
• The time complexity of an algorithm can be represented using asymptotic notation. Big O notation is commonly used to calculate an algorithm's time complexity.

Space Complexity

• The space complexity of an algorithm is a measurement of how much memory it will take to run an algorithm completely, depending on the size of the input.
• Big O notation is also used to compute it.