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Local linearization method information


In numerical analysis, the local linearization (LL) method is a general strategy for designing numerical integrators for differential equations based on a local (piecewise) linearization of the given equation on consecutive time intervals. The numerical integrators are then iteratively defined as the solution of the resulting piecewise linear equation at the end of each consecutive interval. The LL method has been developed for a variety of equations such as the ordinary, delayed, random and stochastic differential equations. The LL integrators are key component in the implementation of inference methods for the estimation of unknown parameters and unobserved variables of differential equations given time series of (potentially noisy) observations. The LL schemes are ideals to deal with complex models in a variety of fields as neuroscience, finance, forestry management, control engineering, mathematical statistics, etc.

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Local linearization method

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analysis, the local linearization (LL) method is a general strategy for designing numerical integrators for differential equations based on a local (piecewise)...

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Linearization

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point of interest. In the study of dynamical systems, linearization is a method for assessing the local stability of an equilibrium point of a system of nonlinear...

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C3 linearization

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multiple superclasses." C3 superclass linearization is an algorithm used primarily to obtain the order in which methods should be inherited in the presence...

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Linear multistep method

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Linear multistep methods are used for the numerical solution of ordinary differential equations. Conceptually, a numerical method starts from an initial...

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Numerical analysis

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Bell Prize Interval arithmetic List of numerical analysis topics Local linearization method Numerical differentiation Numerical Recipes Probabilistic numerics...

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Least squares

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applying local quadratic approximation to the likelihood (through the Fisher information), the least-squares method may be used to fit a generalized linear model...

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Partially linear model

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estimator  After that, in 1997, local linear method was found by Truong. The algebra expression of partially linear model is written as: y i = δ T i...

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Linear programming

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Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical...

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Iterative method

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elimination). Iterative methods are often the only choice for nonlinear equations. However, iterative methods are often useful even for linear problems involving...

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Ridge regression

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Tikhonov, it is a method of regularization of ill-posed problems. It is particularly useful to mitigate the problem of multicollinearity in linear regression...

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Gradient descent

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Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for finding a local minimum of a differentiable...

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Simplex algorithm

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optimization, Dantzig's simplex algorithm (or simplex method) is a popular algorithm for linear programming. The name of the algorithm is derived from...

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Partial differential equation

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correspondingly a vast amount of modern mathematical and scientific research on methods to numerically approximate solutions of certain partial differential equations...

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Nonlinear programming

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and general methods from convex optimization can be used in most cases. If the objective function is quadratic and the constraints are linear, quadratic...

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Penalty method

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Penalty methods are a certain class of algorithms for solving constrained optimization problems. A penalty method replaces a constrained optimization...

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Local regression

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years before LOESS). LOESS and LOWESS thus build on "classical" methods, such as linear and nonlinear least squares regression. They address situations...

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Line search

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enough to the local minimum, but might diverge otherwise. Safeguarded curve-fitting methods simultaneously execute a linear-convergence method in parallel...

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Linear regression

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consideration in formulating a linear regression model, as it will determine the appropriate estimation method. Fitting a linear model to a given data set...

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Big M method

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operations research, the Big M method is a method of solving linear programming problems using the simplex algorithm. The Big M method extends the simplex algorithm...

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Successive linear programming

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" Sequential quadratic programming Sequential linear-quadratic programming Augmented Lagrangian method (Nocedal & Wright 2006, p. 551) (Bazaraa, Sherali...

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Linear approximation

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range, the linear approximation is inadequate and a more detailed analysis and understanding should be used. Binomial approximation Euler's method Finite...

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Euler method

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calculi integralis (published 1768–1770). The Euler method is a first-order method, which means that the local error (error per step) is proportional to the...

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Finite element method

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The finite element method (FEM) is a popular method for numerically solving differential equations arising in engineering and mathematical modeling. Typical...

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