What is algorithm in computer system? Types of algorithms used in computer system

Algorithms play a crucial role in computer science and programming because they systematically and efficiently solve problems. A developer defines an algorithm as a sequence of steps or instructions designed to achieve a specific objective or solve a particular issue. Computers or other computational devices usually carry out these instructions.

Programmers use algorithms as one of the most basic tools to develop problem-solving logic. They define an algorithm as a finite sequence of explicit instructions that, when given a set of input values, produces an output and then terminates.

• For a process to qualify as an algorithm, its steps must be clear and must solve the problem in a finite number of steps. However, it can include iterations and decisions to handle logic and comparisons until the task is complete.
• Different algorithms may accomplish the same task, with a different set of instructions, in more or less the same time, space, and efforts. For example, two different recipes for preparing tea, one ‘add the sugar’ while ‘boiling the water’ and the other ‘after boiling the water’ produce the same result.
• However, executing an algorithm correctly does not guarantee a solution if the algorithm contains flaws or does not suit the context. For example, the tea-preparation algorithm fails if no tea leaves exist, even when someone performs all the steps as though the tea leaves were available. People consciously or subconsciously apply algorithms in their daily lives. Below are some sample algorithms.

Algorithms must have clearly defined steps with specific instructions that leave no room for misinterpretation.

An algorithm should not run an infinite number of steps, thus it should pass through a stage of finite ones.

The main task is to convert a given input into an output, and it must be single or multiple inputs, without any output.

Note in particular that with the same input, an algorithm should always give the same result when it is executed.

These should be realizable step by step, deserving of being executable within the timeline and resource budget.

Algorithms can be categorized into different groups depending on their structure, objective, and level of difficulty. Here are a few typical algorithms employed in computer systems.

Sequential algorithms (Linear algorithms) carry out a given procedure stepwise according to the used sequence. They are point-to-subtle and can be implemented easily; however, they won't be efficient in handling large-scale issues.

This approach mimics divide and conquer tactics in that it splits a problem into several small sub problems which are then independently solved and the solutions combined to return the final one. Examples might involve merging sorting algorithm and quicksort that works for sorting of data.

Recursive algorithms are able to solve issues through a process of decomposing them into smaller, similar cases of the problem at hand. They depend on recursive mechanisms that refer to themselves or similar processes, which present the same input but with a different dimension, until an end-case is reached. Checking of factors and the traverse of the binary tree is an illustration of the recursive algorithms.

Greedy algorithms are the algorithms which have the aim- to make the locally most beneficial decision at any step with the expectation of arriving at the global optimum. This is why they are usually helpful in applications as small as a minimum spanning tree and the shortest path finding, say, that of Dijkstra's algorithm

Dynamic programming algorithms tackle complex problems with after gradually dividing them into smaller subproblems and storing the solved answers to work repetitive calculations. This approach is often the available option for problems which have the common substructure, like the Fibonacci sequence or the knapsack; it is therefore, the best approach.

Randomizing algorithms add randomized inputs or randomizing zicks to the process. On several occasions the randomness that they bring can lead to either a more robust solution or provide better estimations in bodies of calculation such as Monte Carlo algorithms and the such.

As its name implies, a parallel algorithm is an algorithm that performs several instructions at a time by taking advantage of the parallel processing technology in modern computer systems. They are there to optimize huge-scale problems solving, splitting it into fractional parts and managing the work among various processors or cores.

Heuristic algorithms offer rough solutions to difficult problems when finding an exact solution is unfeasible or requires too much computational power. They employ guidelines or tactics drawn from past experiences to help direct the quest for a resolution. Genetic algorithms and simulated annealing are instances of heuristic algorithms.

Algorithms are key in computer systems for solving problems and making decisions. Programmers must understand different algorithms to build strong, scalable solutions. Using the right algorithms improves efficiency, system performance, and user satisfaction.