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Software . 2023
License: CC BY
Data sources: Datacite; ZENODO
ZENODO
Software . 2023
License: CC BY
Data sources: Datacite
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luclaurent/gradFD: gradFD: a MATLAB/OCTAVE's class for computing gradients and hessians of a function using finite differences

Authors: luclaurent;

luclaurent/gradFD: gradFD: a MATLAB/OCTAVE's class for computing gradients and hessians of a function using finite differences

Abstract

gradFD is a MATLAB/OCTAVE's class which can be used for computing the derivatives and hessians of a function using finite differences. Many schemes have been implemented. Notice that the hessian computation remains incomplete and needs to be improved. Features gradFD is able to Compute derivatives with the following schemes forward and backward finite differences of order 1 to 5 (BDx and FDx with x={1,...,5}) central finite differences of order 2 to 8 (CDx with x={2,4,6,8}) Minimize the number of computations (especially the responses at the central points is done only one time) Use a specific stepsize in every direction Generate the set of sample points which can be used externally for computing responses. These responses can be loaded by the class in order to compute the gradients. First start Many example script have been proposed Example_basic_gradFD.m: basic example of a classical use of the class Example_external_gradFD.m: example of use of the external feature Example_plot1D_gradFD.m: plot derivatives compute on 1D function with the class Example_plot2D_gradFD.m: plot derivatives compute on 2D function with the class Example_error_gradFD.m: study of the error on derivatives vs the stepsize The following picture is the result of the execution of the Example_plot2D_gradFD.m script: The following picture is the result of the execution of the Example_error_gradFD.m script: Use of the class The class could be called using the following syntax gradFD(typeIn,XrefIn,stepsIn,funIn) where typeIn is the chosen schemes (the full list of finite difference schemes is available via the execution of gradFD with no option or via the method displaySchemes). XrefIn is the array of points on which the gradient must be calculated. stepsIn is the list of stepsizes used in every direction and/or every point. funIn is the handle function (defined using @()) [optional] If all these arguments are given to the class the gradients are calculated directly and stored in the property GZeval. In the case of an external computation of the responses, the sample points can be exported usind the property XevalG and the responses computed externally can be loaded using the method loadZextG (see for instance the example Example_external_gradFD.m) Full Changelog: https://github.com/luclaurent/gradFD/compare/v2.2.1...v2.2.2

Keywords

gradients, optimization, finite-differences, Matlab

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average