Symbolic Computation is a field of computer science that combines mathematics and computer science to solve problems. It involves concepts such as symbolic representations, abstraction and automation. Symbolic computation methods can be used to manipulate symbols and numerical data, solve mathematical equations and analyze algorithms.
Symbolic Computation utilizes a combination of mathematics, logic, computer science, and artificial intelligence (AI). This combination doesn’t just explore the theoretical aspects of each discipline, but the practical sides of them as well. Symbolic Computation enables researchers to answer highly complex questions or problems of more than one scientific domain.
Symbolic computing includes several areas such as computation theorem proving, symbolic analysis of equations and formulas, automated reasoning, and computer algebra systems. It also involves automated deduction, logic programming, symbolic integration, and symbolic/numerical integration.
In Symbolic Computation, symbols are used to represent operations and symbolic constraints are used to control subsequent operations. Symbolic constraints are often used to optimize results. This kind of computation allows knowledgeable humans to apply their knowledge in order to use symbolic manipulation to prove certain mathematical properties.
Symbolic Computation is used in a number of fields such as physics and cosmology, biological sciences, education, engineering, game theory, dynamic system, computational finance, and artificial intelligence (AI). The knowledge used and the tools employed in symbolic computation can be applied in a variety of ways. Some practical uses of symbolic computation include optimization problems, mathematical analysis, circuit synthesis, language processing, and computing functions.
