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

Algorithms are crucial in computer science and programming as they are key to solving problems in a systematic and efficient manner. An algorithm is a defined as a sequence of steps or instructions created to achieve a particular objective or address an issue. These directions are usually carried out by a computer or another type of computational device.

Algorithms are one of the most basic tools that are used to develop the problem-solving logic. An algorithm is defined as a finite sequence of explicit instructions that, when provided with a set of input values,produces an output and then terminates.

• To be an algorithm, the steps must be unambiguous and after a finite number of steps the solution of the problem is achieved. However, algorithms can have steps that repeat (iterate) or require decisions (logic and comparison) until the task is completed.
• 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, performing an algorithm correctly does not guarantee a solution, if the algorithm is flawed or not appropriate to the context. For example, preparing the tea algorithm will fail if there are no tealeaves present; even if all the steps of preparing the tea are performed as if the tea leaves were there. Consciously or subconsciously, we use algorithms in our daily life.Here are some sample algorithms.

The algorithms should have steps with clear definition and provided specific instructions that cannot be misinterpreted.

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 essential components in computer systems that facilitate effective problem-solving and decision-making procedures. It is crucial for programmers and software developers to comprehend the characteristics of various algorithms in order to create strong and scalable solutions in different fields. Utilizing appropriate algorithmic methods can lead to more efficient completion of computational tasks, resulting in enhanced system performance and user satisfaction.