Function approximation using gradient information with application to parametric and stochastic differential equations

Gleb Ryzhakov; Ivan Oseledets

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arxiv 1802.01542 v1 pith:ABMQJALR submitted 2018-02-05 math.NA cs.NA

Function approximation using gradient information with application to parametric and stochastic differential equations

Gleb Ryzhakov , Ivan Oseledets This is my paper

classification math.NA cs.NA

keywords functionapproximationderivativesinformationproblemaccuracyaddingadjust

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In the paper we consider the problem of multivariate function approximation in polynomial basis. In order to solve this problem, we adjust the least squares method (LSM) by adding information about derivatives of the function. This modification allows reducing the number of evaluations of approximating function while keeping the accuracy at the appropriate level. We propose several techniques for time-efficient calculation of derivatives in various applications. Numerical examples are given for comparison between the standard LSM and the proposed approach.

Function approximation using gradient information with application to parametric and stochastic differential equations

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