How to make an Algorithm from scratch: Everything you need to know

Last update: 14 June, 2025
Author Dr369
  • Algorithms are ordered sequences of instructions for solving specific problems in technology.
  • An effective algorithm must be accurate, finite, efficient, and generalizable to different data sets.
  • There are various types of algorithms, such as search, sorting, and machine learning, with multiple real-world applications.
  • Optimization and complexity analysis are crucial to improving the performance of implemented algorithms.
How to make an algorithm

In today's digital world, algorithms are at the heart of every technological solution we use daily. From Google searches to Netflix recommendations, algorithms are working tirelessly to process data and make decisions. But what exactly is an algorithm, and how do you create one from scratch? In this article, I'll guide you through the fascinating process of algorithm creation, providing you with the tools and knowledge needed to master this fundamental skill in computer science and programming.

How to make an Algorithm from scratch: Everything you need to know

Meaning of Algorithm

Algorithms are not only a crucial part of software development, but they are also essential in fields such as artificial intelligence, data analysis, and process optimization. Mastering the art of creating algorithms will allow you to solve complex problems efficiently, improve your logical thinking skills, and stand out in the competitive world of technology.

Throughout this article, we'll explore the fundamental concepts, best practices, and advanced techniques for designing effective algorithms. Whether you're a curious beginner or an experienced programmer looking to hone your skills, this comprehensive guide will provide you with the knowledge you need to create robust and efficient algorithms from scratch.

In short, the meaning of an algorithm is as follows: An algorithm is an ordered and finite set of steps or instructions that describes how to solve a problem or perform a specific task. It is fundamental in computing and programming because it provides a logical and detailed sequence of operations that must be carried out to achieve a desired result. Algorithms are the foundation upon which computer programs and automated systems are built to solve problems efficiently and systematically.

How to Make an Algorithm: Fundamentals and Basic Concepts

Before we dive into the process of creating algorithms, it is essential to understand what exactly an algorithm is and what its essential features are.

Definition and characteristics of an efficient algorithm

An algorithm is, in essence, a set of step-by-step instructions designed to solve a specific problem or perform a given task. But not any sequence of steps can be considered an efficient algorithm. For an algorithm to be truly effective, it must meet certain key characteristics:

  1. Precision:Each step of the algorithm must be clearly defined and unambiguous.
  2. Finitude: The algorithm must terminate after a finite number of steps.
  3. Defined input and output: It must have clearly specified inputs and produce expected outputs.
  4. Efficiency: You must solve the problem in a reasonable time and with optimal use of resources.
  5. Generality: It should be able to handle different input data sets within its domain.

A simple example of an algorithm could be the process for making a cup of coffee:

  1. Fill the coffee maker with water.
  2. Place a filter in the filter holder.
  3. Add ground coffee to the filter.
  4. Turn on the coffee maker.
  5. Wait until the coffee is ready.
  6. Serve the coffee in a cup.

This example, although simple, illustrates how an algorithm breaks down a task into clear, executable steps.

Types of algorithms and their application in the real world

Algorithms can be classified in a variety of ways, depending on their structure, purpose, or implementation method. Some common types of algorithms include:

  1. search algorithms: Used to find a specific item in a data set. Examples include binary search and linear search.
  2. Sorting algorithms: Designed to organize data in a specific order. Popular algorithms include quicksort and mergesort.
  3. Graph algorithms: Used to solve problems related to graph data structures, such as finding the shortest path between two points.
  4. Machine learning algorithms: Used in artificial intelligence to allow machines to learn from data and improve their performance over time.
  5. compression algorithms: Designed to reduce data size for more efficient storage or transmission.
  Mosca's Theorem and the arrival of quantum computing

In the real world, algorithms have virtually unlimited applications. For example:

  • Search engines use complex algorithms to rank and present relevant results.
  • Social media networks use algorithms to personalize the content you see in your feed.
  • GPS navigation systems use algorithms to calculate the most efficient route between two points.
  • Recommendation systems on streaming or e-commerce platforms use algorithms to suggest products or content based on your preferences.

Understanding these basic concepts is crucial to start creating your own algorithms. In the next section, we'll dive into the step-by-step process of designing an algorithm from scratch.

Steps to create an algorithm from scratch

How to create an algorithm is a common question among computer scientists and students. Creating an effective algorithm requires a methodical and structured approach. By following these steps, you'll be able to develop logical and efficient solutions to a wide range of problems.

Problem identification and definition of objectives

The first crucial step in creating any algorithm is to clearly understand the problem you are trying to solve. This process involves:

  1. Define the problem: Articulate the specific challenge or task that the algorithm must address. For example, “Sort a list of numbers from smallest to largest.”
  2. To establish objectives: Determine what exactly the algorithm should achieve. In our example, the goal would be “Produce a sorted list of numbers in ascending order.”
  3. Identify constraints: Consider any special limitations or requirements. This could include runtime restrictions, memory usage, or specific data types.
  4. determine the scope: Clearly define which aspects of the problem your algorithm will address and which will be beyond its scope.

Once you have clearly defined your problem and objectives, you will be in a better position to design an effective solution.

Analysis of input data and expected output

The next step is to thoroughly understand the data your algorithm will be working with:

  1. Identify input data: What information will your algorithm receive? In our sorting example, it would be an unordered list of numbers.
  2. Determine the input format: How will this data be presented? Will it be a list, an array, a text file?
  3. Define the expected output: What should your algorithm produce? In our case, it would be an ordered list of numbers.
  4. Consider special cases: Think of extreme or unusual situations. What should your algorithm do if the list is empty or if all the numbers are equal?

This analysis will help you design an algorithm that can effectively handle all possible scenarios.

Design of the logic and structure of the algorithm

With a clear understanding of the problem and the data, you can begin to design the logic of your algorithm:

  1. Divide the problem into subproblems: Break down the main problem into smaller, manageable steps.
  2. Develop an overall strategy: Decide which approach you will use to solve the problem. For our sorting example, you could choose a method such as bubble sort or quick sort.
  3. Outline the main steps: Create a high-level outline of the steps your algorithm will follow.
  4. Refine each step: Develop the details of each step, considering how to handle different scenarios and edge cases.
  5. Consider efficiency: Think about how you can optimize your algorithm to be as efficient as possible in terms of time and resource usage.

For example, an initial outline for our sorting algorithm might be:

  1. Receive the unordered list.
  2. Compare adjacent elements.
  3. Swap items if they are in the wrong order.
  4. Repeat the process until no more exchanges are needed.
  5. Return the sorted list.

This initial design provides a solid foundation for developing a more detailed and refined algorithm. Let's continue to discover how to make an Algorithm.

Tools and techniques for creating algorithms

To transform your conceptual design into a working algorithm, there are several tools and techniques you can use. These will help you visualize, plan, and communicate your algorithm effectively.

Pseudocode and flowcharts: Their importance in design

Pseudocode and flowcharts are invaluable tools in the algorithm design process, as they allow you to represent the logic of your solution in a clear and structured way before diving into the actual coding.

  Introduction to Algorithms: A Complete Guide

Pseudocode : Pseudocode is a high-level, informal description of an algorithm that uses a mixture of natural language and simplified programming structures. It is especially useful because:

  1. Makes it easier to plan and organize your ideas.
  2. It's easier to read and understand than actual code.
  3. It allows you to focus on the logic without worrying about the specific syntax of a programming language.

Example pseudocode for our sorting algorithm:

FUNCIÓN ordenar(lista):
n = longitud de lista
PARA i DESDE 0 HASTA n-1:
PARA j DESDE 0 HASTA n-i-1:
SI lista > lista:
intercambiar lista y lista
DEVOLVER lista

Flowcharts : Flowcharts are graphical representations of the flow of control in an algorithm. They are useful because:

  1. They provide a clear visualization of the process.
  2. They help identify loops, conditions and decision points.
  3. They facilitate the communication of the algorithm's logic to others.

A simple flowchart for our sorting algorithm might look like this:

→ → → (Sí) → →
↓ (No)

↓
→ (Sí) →
↓ (No)

↓

 

Programming languages ​​suitable for implementing algorithms

Once you have designed your algorithm using pseudocode and flowcharts, the next step is to implement it in a real programming language. The choice of language will depend on several factors, including:

  1. The nature of the problem: Some languages ​​are better suited for certain types of algorithms or applications.
  2. Required efficiency: Certain languages ​​offer better performance for specific tasks.
  3. Familiarity and experience: It is easier to implement algorithms in languages ​​you know well.
  4. Resources available: Consider the libraries and tools available in each language.

Some popular languages ​​for implementing algorithms include:

  • Python: Great for rapid prototyping and easy to read. Has a wide range of libraries for algorithms and data structures.
  • C++: Offers high performance and low-level control, ideal for algorithms that require maximum efficiency.
  • Java: Provides a good balance between performance and ease of use, with a large community and resources.
  • JavaScript: Useful for algorithms that will run in web browsers or Node.js environments.
  • R: Specialized in statistical algorithms and data analysis.

For example, our sorting algorithm implemented in Python might look like this:

python
def ordenar(lista):
n = len(lista)
for i in range(n):
for j in range(0, n - i - 1):
if lista > lista:
intercambiar lista y lista
return lista

Remember that your choice of language should be based on the specific needs of your project and your own skills and preferences.

Optimization and improvement of algorithms

We already know how to make an Algorithm. Once you have implemented your algorithm, the next crucial step is to optimize it to improve its efficiency and performance. Algorithm optimization is an ongoing process that can make the difference between a solution that works and one that excels.

Algorithmic complexity and efficiency analysis

Complexity analysis is a fundamental tool for evaluating and improving the efficiency of an algorithm. It focuses on how the algorithm's execution time and memory usage grow as the size of the input data increases. The two main types of complexity that are analyzed are:

  1. Time complexity: Measures how long the algorithm takes to run based on the size of the input.
  2. Space complexity: Evaluates how much memory the algorithm uses during its execution.

Big O notation is the most common way to express algorithmic complexity. For example:

  • O(1): Constant time (ideal)
  • O(log n): Logarithmic time (very efficient)
  • O(n): Linear time (efficient)
  • O(n log n): Logarithmic linear time (quite efficient)
  • O(n²): Quadratic time (may be problematic for large data sets)
  • O(2^n): Exponential time (generally inefficient for large problems)

For our bubble sort algorithm example, the time complexity is O(n²) in the worst case, which means it is not very efficient for large lists.

To improve efficiency, you might consider implementing a more efficient sorting algorithm such as quicksort, which has an average complexity of O(n log n):

python
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr
left =
middle =
right =
return quicksort(left) + middle + quicksort(right)

This algorithm is significantly more efficient for large lists.

Algorithm debugging and testing techniques

Debugging and testing are essential to ensure that your algorithm works correctly and efficiently. Some useful techniques include:

  1. Unit tests: Write tests for each component of your algorithm.
  2. Boundary Test Cases: Test your algorithm with edge cases (empty lists, lists of a single element, etc.).
  3. Test of performance: Measures execution time and memory usage for different input sizes.
  4. Step-by-step debugging: Use a debugger to follow the execution of your algorithm line by line.

Example of unit tests for our sorting algorithm:

python

import unittest

class TestQuicksort(unittest.TestCase):
def test_sort_empty_list(self):
self.assertEqual(quicksort(), )

def test_sort_list_one_element(self):
self.assertEqual(quicksort(), )

def test_sort_unordered_list(self):
self.assertEqual(quicksort(),

if __name__ == '__main__':
unittest.main()

These tests help verify that your algorithm works correctly in different scenarios.

quantitative algorithm
Related articles:
Quantitative Algorithm: 7 Keys to Mastering Automated Trading
How to make an Algorithm How to make an Algorithm

How to make an Algorithm: Practical Application

Now that we've covered the basics and advanced techniques, let's see how to apply all this in a practical example. Suppose we want to create an algorithm to find the most frequent number in a list.

python

from collections import Counter

def most_frequent_number(list):
if not list:
return None
counter = Counter(list)
return counter.most_common(1)

# Example of use
numbers =
print(«The most frequent number is:», most_frequent_number(numbers))

This algorithm uses the class Counter Python to count the occurrences of each number and then returns the most frequent one. Its time complexity is O(n), where n is the number of elements in the list, making it quite efficient.

FAQ: How to make an Algorithm 

What is the difference between an algorithm and a computer program?

An algorithm is a set of logical steps to solve a problem, while a computer program is the implementation of one or more algorithms in a specific programming language. Algorithms are language-independent, while programs are tied to a particular language.

How can I improve my algorithm creation skills?

Practice regularly solving algorithmic problems, participate in online coding challenges, study data structures and classical algorithms, and analyze other programmers' solutions. Constant practice and exposure to diverse problems are key to improvement.

What tools can I use to visualize my algorithms?

There are several useful tools such as draw.io for creating flowcharts, PythonTutor for visualizing code execution step by step, and profiling tools in IDEs such as PyCharm or Visual Studio Code for analyzing performance.

How do I choose the best algorithm for a specific problem?

Consider factors such as time and space complexity, the nature of the input data, performance requirements, and ease of implementation and maintenance. It is often useful to implement and compare multiple solutions to find the optimal one.

Do algorithms always guarantee the best solution?

Not always. Some problems are so complex that finding the optimal solution may be computationally infeasible. In these cases, approximation algorithms or heuristics are used that provide “good enough” solutions in a reasonable time.

How can I handle large data sets in my algorithms?

For large data sets, consider techniques such as batch processing, parallelization, the use of efficient data structures (such as trees or hash tables), and algorithms designed specifically for big data, such as MapReduce.

What is a conventional algorithm
Related articles:
What is a Conventional Algorithm and Why Should You Care?