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General linear methods — a class of methods encapsulating linear multistep and Runge-Kutta methods; Bulirsch–Stoer algorithm — combines the midpoint method with Richardson extrapolation to attain arbitrary order; Exponential integrator — based on splitting ODE in a linear part, which is solved exactly, and a nonlinear part
In numerical analysis, a numerical method is a mathematical tool designed to solve numerical problems. The implementation of a numerical method with an appropriate convergence check in a programming language is called a numerical algorithm.
The field of numerical analysis predates the invention of modern computers by many centuries. Linear interpolation was already in use more than 2000 years ago. Many great mathematicians of the past were preoccupied by numerical analysis, [5] as is obvious from the names of important algorithms like Newton's method, Lagrange interpolation polynomial, Gaussian elimination, or Euler's method.
In numerical analysis, the Runge–Kutta methods (English: / ˈ r ʊ ŋ ə ˈ k ʊ t ɑː / ⓘ RUUNG-ə-KUUT-tah [1]) are a family of implicit and explicit iterative methods, which include the Euler method, used in temporal discretization for the approximate solutions of simultaneous nonlinear equations. [2]
A method based on numerical inversion of a complex Laplace transform was developed by Abate and Dubner. [21] An algorithm that can be used without requiring knowledge about the method or the character of the function was developed by Fornberg. [4]
Chapter Seven of The Nine Chapters on the Mathematical Art also deals with solving a system of two equations with two unknowns with the false position method. [20] To solve for the greater of the two unknowns, the false position method instructs the reader to cross-multiply the minor terms or zi (which are the values given for the excess and ...
Numerical methods for ordinary differential equations, such as Runge–Kutta methods, can be applied to the restated problem and thus be used to evaluate the integral. For instance, the standard fourth-order Runge–Kutta method applied to the differential equation yields Simpson's rule from above.
These people become known as "financial engineers" ("quant" is a term that includes both rocket scientists and financial engineers, as well as quantitative portfolio managers). [13] This led to a second major extension of the range of computational methods used in finance, also a move away from personal computers to mainframes and ...