The theory of computation is a topic under computer science that determines how productively a system can solve problems. It analyzes the efficiency of a system in solving problems with the help of an algorithm. Similarly, it also examines the basic fundamentals of computation. This assists in expanding the efficiency of the system. In most cases, we can achieve this by identifying the validity of solutions. Furthermore, this branch under computer is rapidly gaining popularity.
Importance of Turing Machine in Theory of Computation
Before we hop into the customary aspects of the theory of computation, let us understand the Turing machine. It is a theoretical, conceptual machine that acts as the model of computation. Besides, this machine plays a prominent role in explaining the speculation of computation. Turing machine utilizes uncountable memory tape where we store all the information. Additionally, it also examines this information to decide if the operation is pragmatic or not. Alan Turing introduced this machine to the world in the year 1963.
How to Approach This Speculation?
We can approach this hypothesis through three primary fields and they are as follows:
- Automata Theory
- Computability Theory
- Computational Complexity Theory
These three fields in general make the most of the power of the computer in resolving issues. On top of that, the algorithms exceedingly assist them. Here, we will discuss all three branches in brief.
This field mainly examines the functioning and performance of a system. Besides, it also inspects how a problem is solved. The most brawny model of automata is the Turing Machine. This theory first came into play in the 20th century.
In this conception, we can map out the issues with assistance from the Turing Machine. In simple words, we can formulate the issues in a facile way. However, the downside is that we cannot resolve these issues in this theory. Here, the system can only find the problem but cannot find a solution to that problem.
Computational Complexity Theory
The computational complexity theory refers to the quality at which we can solve a problem. Furthermore, in this conception, we consider two predominant elements; time complexity and space complexity. These are the estimations of the total number of steps required to examine and solve the problem. Therefore, it finds out the total memory space needed to solve a problem. Because time and space increase linearly, we can predetermine these factors. We can do so by associating these two elements with the amount of input.