Discrete mathematics
Discrete mathematics Discrete mathematics is the study of mathematical structures that are fundamentally discrete rather than continuous. Real numbers and rational numbers have the property that between any two numbers a third can be found, and consequently these numbers vary "smoothly". The objects generally studied in discrete mathematics – such as is the common name for the fields of mathematics most generally useful in theoretical computer science Theoretical computer science is the collection of topics of computer science that focuses on the more abstract, logical and mathematical aspects of computing, such as the theory of computation, analysis of algorithms, and semantics of programming languages. Although not itself a single topic, its practitioners form a distinct subgroup within. This includes computability theory, computational complexity theory, and information theory Information theory is a branch of applied mathematics and electrical engineering involving the quantification of information. Historically, information theory was developed by Claude E. Shannon to find fundamental limits on compressing and reliably storing and communicating data. Since its inception it has broadened to find applications in many. Computability theory examines the limitations of various theoretical models of the computer, including the most powerful known model – the Turing machine Turing machines are basic abstract symbol-manipulating devices which, despite their simplicity, can be adapted to simulate the logic of any computer algorithm. They were described in 1936 by Alan Turing. Turing machines are not intended as a practical computing technology, but a thought experiment about the limits of mechanical computation. Thus. Complexity theory is the study of tractability by computer; some problems, although theoretically solvable by computer, are so expensive in terms of time or space that solving them is likely to remain practically unfeasible, even with rapid advance of computer hardware. Finally, information theory is concerned with the amount of data that can be stored on a given medium, and hence deals with concepts such as compression In computer science and information theory, data compression or source coding is the process of encoding information using fewer bits than an unencoded representation would use through use of specific encoding schemes and entropy In information theory, entropy is a measure of the uncertainty associated with a random variable. The term by itself in this context usually refers to the Shannon entropy, which quantifies, in the sense of an expected value, the information contained in a message, usually in units such as bits. Equivalently, the Shannon entropy is a measure of the.
As a relatively new field, discrete mathematics has a number of fundamental open problems. The most famous of these is the "P=NP? The relationship between the complexity classes P and NP is an unsolved question in theoretical computer science. It is considered to be the most important problem in the field – the Clay Mathematics Institute has offered a $1 million US prize for the first correct proof" problem, one of the Millennium Prize Problems.[32]
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