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In numerical analysis, predictor–corrector methods belong to a class of algorithms designed to integrate ordinary differential equations – to find an unknown function that satisfies a given differential equation. All such algorithms proceed in two steps:
The Adams–Moulton methods are solely due to John Couch Adams, like the Adams–Bashforth methods. The name of Forest Ray Moulton became associated with these methods because he realized that they could be used in tandem with the Adams–Bashforth methods as a predictor-corrector pair (Moulton 1926); Milne (1926) had the same idea.
Download as PDF; Printable version ... serves as the basis to construct more complex methods, e.g., predictor–corrector ... by the two-step Adams–Bashforth method:
Pages for logged out editors learn more. Contributions; Talk; Adams-Bashforth method
Mehrotra's predictor–corrector method in optimization is a specific interior point method for linear programming.It was proposed in 1989 by Sanjay Mehrotra. [1]The method is based on the fact that at each iteration of an interior point algorithm it is necessary to compute the Cholesky decomposition (factorization) of a large matrix to find the search direction.
For linear multistep methods, an additional concept called zero-stability is needed to explain the relation between local and global truncation errors. Linear multistep methods that satisfy the condition of zero-stability have the same relation between local and global errors as one-step methods.
The application of MacCormack method to the above equation proceeds in two steps; a predictor step which is followed by a corrector step. Predictor step: In the predictor step, a "provisional" value of u {\displaystyle u} at time level n + 1 {\displaystyle n+1} (denoted by u i p {\displaystyle u_{i}^{p}} ) is estimated as follows
Adams method may refer to: A method for the numerical solution of ordinary differential equations, also known as the linear multistep method A method for apportionment of seats among states in the parliament, a kind of a highest-averages method