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Introduction to numerical analysis by the Department of Mathematics at UC Santa Barbara.
Learn the principles of various numerical techniques for solving nonlinear equations, performing integrations, and solving differential equations by the Runge-Kutta methods. Learn the fact that numerical methods offer approximate but credible accurate solutions to the problems that are not readily or possibly solved by closed-form
Numerical analysis provides the foundations for a major paradigm shift in what we understand as an acceptable “answer” to a scientific or techni- cal question.
Data analysis. Can we make sense of 17-dimensional data? (FDTD. Ever wondered about those realistic physics in computer games?) Number representation and oating point computation. Teach computers to represent and deal with numbers ... and ourselves to deal with the fallout. Numerical Analysis 2 Easter Term 2018/19
mathematical problems in numerical form. In numerical analysis we are mainly interested in implementation and analysis of numerical algorithms for finding an approximate solution to a mathematical problem.
Worked examples and targeted exercises enable the student to master the realities of using numerical techniques for common needs such as the solution of ordinary and partial differential equations, fitting experimental data, and simulation using particle and Monte Carlo methods.
Numerical Analysis - The study of algorithms (methods) for problems involving quantities that take on continuous (as opposed to discrete) values. Numerical Calculation vs. Symbolic Calculation. Numerical Calculation: involve numbers directly. I manipulate numbers to produce a numerical result. Symbolic Calculation: symbols represent numbers.
