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Richard F. Johnsonbaugh (born 1941) [1] is an American mathematician and computer scientist. His interests include discrete mathematics and the history of mathematics. He is the author of several textbooks. Johnsonbaugh earned a bachelor's degree in mathematics from Yale University, and then moved to the University of Oregon for graduate study. [2]
This university learning plan consists of a primer on discrete mathematics and its applications including a brief introduction to a few numerical analysis.. It has a special focus on dialogic learning (learning through argumentation) and computational thinking, promoting the development and enhancement of:
Discrete mathematics is the study of mathematical structures that can be considered "discrete" (in a way analogous to discrete variables, having a bijection with the set of natural numbers) rather than "continuous" (analogously to continuous functions).
Discrete mathematics is the study of mathematical structures that are fundamentally discrete rather than continuous.In contrast to real numbers that have the property of varying "smoothly", the objects studied in discrete mathematics – such as integers, graphs, and statements in logic [1] – do not vary smoothly in this way, but have distinct, separated values. [2]
Numerical analysis is the study of algorithms that use numerical approximation (as opposed to general symbolic manipulations) for the problems of mathematical analysis (as distinguished from discrete mathematics). [25] Modern numerical analysis does not seek exact answers, because exact answers are often impossible to obtain in practice.
Discrete Mathematics is a biweekly peer-reviewed scientific journal in the broad area of discrete mathematics, combinatorics, graph theory, and their applications. It was established in 1971 and is published by North-Holland Publishing Company .
Dichotomization is the special case of discretization in which the number of discrete classes is 2, which can approximate a continuous variable as a binary variable (creating a dichotomy for modeling purposes, as in binary classification). Discretization is also related to discrete mathematics, and is an important component of granular computing.
[5] In signal processing and machine learning, discrete calculus allows for appropriate definitions of operators (e.g., convolution), level set optimization and other key functions for neural network analysis on graph structures. [3] Discrete calculus can be used in conjunction with other mathematical disciplines.