What is the programming algorithm?

An algorithm is a programming order that includes a series of instructions that define the necessary steps to perform and complete an activity or to obtain a certain result. Algorithms are fundamental in the processing of data and in the resolution of problems through computers. In essence, the algorithm receives a problem such as input and provides a solution – output.

The algorithm, from its origins in mathematics, has evolved significantly, now becoming the backbone of IT and programming. The algorithm not only organizes the necessary steps to solve computer problems, but is the basis of each software, being the tool that allows the transformation of abstract ideas in the reality of the code in execution on your gadgets. The origins of the algorithm can be found in the fundamental contribution of the Persian mathematician Muhammad al-Khwarizmi, now recognized as a central figure in the development of the algebra. It remains with us to understand what are the algorithms and what are the 4 methods for writing the algorithm in programming.

When we approach certain programming algorithms, various presentation methods are used, including natural language, diagrams, pseudocods algorithms, of course, the code in programming languages. The diagrams, which provide a schematic vision of the problem and its resolution, are often preferred for their simple accessibility.

From the identification of the respective IT problem and from the extraction of a solution, the data and operations must be organized effectively. Here are the three primary data structures used:

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1. Straight: This structure is characterized by a series of controls performed in sequence, each once. It is the basis of direct and simplistic programs, without evidence or branches.
2. Branched: In the branched structure, some orders cannot be performed at all, depending on certain predefined conditions. It is represented by a bifurcation in the algorithm, in which the direction followed depends on the result of the evaluation of the respective conditions; Multiple paths cover various possible scenarios.
3. cyclic: We often find cases where we do not know all the starting data and the cyclical structure allows us to return and perform some parts of the algorithm several times. This constant translation of the steps is called iteration.

Examples of well -known programming algorithms

• Google Hangouts: It transmits live due to audio and video compression algorithms.

• Google Maps: It helps you get from Dallas, Texas to Orlando, Florida and then to Disney World using an algorithm to find a road.

• Pixar: It collects 3D models of lighting characters based in a virtual room, using an algorithm of interpretation/transmission.

Classification of algorithms

Algorithms can be classified according to the approach method:

• Basic backtracking alegorithgi: Explore all the possibilities to find a solution.

• The type of DID is divided EthGowing: Divide the problem into smaller sub -rudems, I solve them separately and combine solutions to obtain the final solution.

• Greedy algorithms: Choose the optimal local solution at each step with the hope of reaching the optimal global solution.

At the same time, algorithms can also be classified according to the type of operations used:

• Logical algorithms: Manipulate logical data (True/False).

• Numerical algorithms: Manage numerical data (whole numbers, real numbers).

• Small algorithms: Combine logical and numerical operations.

Each algorithm is designed to simplify data processing and provide an effective method to deal with simple or complex programming problems. The optimization of algorithms according to the criteria listed above will guarantee their usefulness and efficiency in practice.

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Why is the efficiency of the algorithms important?

• Speed: The efficiency refers to the speed with which an algorithm can solve a problem. An effective algorithm is the one that provides results in a reasonable time. If an algorithm takes too long to complete an operation, it can become not very practical or useless in practice.

• Space-Bar: Efficiency is not limited only over time, but also to the memory space used by the algorithm. An effective algorithm uses resources available optimally. Excessive allocation of memory can affect the overall system performance.

• Optimization: Optimization is the process by which the efficiency of an algorithm has improved. It can be obtained by reducing the execution time or saving the memory space. For example, reuse of pre -calcolated results can improve speed, even if it requires more temporary memory.

• Precision: The passages of the algorithm must be accurately defined, so that there are no more ambiguity or interpretations. Algorithms must be carefully designed and planned to avoid errors.

4 Algorithm writing methods in programming

In the programming program, there are several ways to face the writing of algorithms, each of which has its own specificity.

1. Decomposition and resolution

The approach of complex programming problems can be simplified by decomposition in sub -RUBLIMS more easily to manage. This strategy transmits the resolution process, allowing attention to the component parts of an algorithmic problem and subsequently integrating the results in a unified solution. A representative example would be the sorting method that uses this technique to separate and group the elements before combining them in the correct order.

2. Avid approach

When the context of the final solution is not clear, the most pragmatic method is to try to choose the best option available at that moment. Therefore, you will advance to a resolution, making optimal choices step by step and hoping that they lead to the ideal solution. Unlike dynamic programming, the avid algorithm does not involve the complete preview of some algorithmic problems.

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Dynamic programming is looking for solutions for sub -Rentablemi to carry out the main resolution of problems. Unlike Avid methods, it presupposes that previous solutions can influence the quality of the global solution and therefore re -evaluate the previous decisions to achieve the optimal result.

4. Heuristic strategies

This approach aims to find a satisfactory solution, not necessarily the best, considering the constraints imposed (time, memory, cost, etc.). Heuristic allows the exploration of paths that may not be perfect, but which are good enough to satisfy the established criteria, often require human intervention and the evaluation to choose the most appropriate option among those available.

Our conclusion?

The learning of the algorithm is a precious investment for any programmer, software engineer or student who wants to understand and build efficient and high -performance software solutions. In conclusion, these fundamental algorithms are a key component of computer science and a wide range of fields, offering a solid base for a successful IT career.

Our IT – Newtech Academy courses teaches you to think algorithmic, essential for any programmer. Through our programs you will discover how to logically structure the steps so that your programs are quickly and without errors. Don’t miss the opportunity and get in touch with one of our consultants right now!