Brute Force vs Greedy

Brute Force vs Greedy: two approaches compared

Algorithmic problem solving is a fundamental element in computer science, requiring the application of efficient strategies to obtain optimal or acceptable solutions in terms of time and space. Two distinct approaches in this context are known as Brute Force and Greedy. These represent two ends of the algorithmic complexity spectrum, each with its own advantages and limitations.

Fibonacci Series three algorithms

The Fibonacci Series: three different algorithms compared

The efficiency of algorithms plays a central role in software development, directly influencing the performance and responsiveness of applications. In this context, the Fibonacci series provides fertile ground for exploring and comparing different implementation strategies, from the classic recursive approach to more optimized solutions such as iteration and dynamic programming.

Recursive Algorithms

Recursive Algorithms

A recursive algorithm is an algorithm that solves a problem by dividing it into smaller sub-problems of the same nature. The solution of the overall problem is obtained by combining the solutions of the sub-problems. The recursive approach is based on recursive calling, which consists of invoking the same function (or procedure) within the very definition of that function.

Mergesort & Quicksort

Mergesort and Quicksort: Two Dominant Approaches for Efficient Sorting

Sorting data is one of the fundamental operations in computing, and choosing an appropriate sorting algorithm can significantly affect the performance of an application. Two of the best-known and widely used algorithms are Mergesort and Quicksort. These two approaches, both based on the “divide and conquer” principle, are capable of ordering sequences of data efficiently, but they adopt different strategies to achieve this goal.

Sorting and Searching: algorithms with Python

Searching and sorting are two fundamental operations in computer science that are essential for effectively manipulating and exploring data. In this section, with a series of in-depth articles, we will examine the main search and sorting techniques implemented using the Python programming language, exploring the most common algorithms and their practical applications.

Numerical Algorithms

Numerical Algorithms with Python

In the vast world of numerical algorithms, we dive into concepts and techniques aimed at manipulating and generating numbers. Within numerical algorithms, you can find solutions for problems such as interpolation, solving differential equations, numerical optimization, and more. In this section, we will explore numerical algorithms using the Python programming language with a series of in-depth articles.