MATLAB: Do I get an error regarding “cl_inventor2sm.dll” or “cl_sldwks2sm.dll” when registering Simscape Multibody Link Plug-In in MATLAB R2019b

cl_inventor2sm.dllcl_sldwks2sm.dllSimscapesimscape-multibody-linksmlink_linkinvsmlink_linksw

When calling "smlink_linkinv" to register the Simscape Multibody Plug-In, I see the following error:

The module "E:\MATLAB64\R2019b\bin\win64\cl_inventor2sm.dll" failed to load.
Make sure the binary is stored at the specified path or debug it to check for problems with the binary or dependent .DLL files.
The specified module could not be found.

The same error is shown when calling "smlink_linksw", but the module that fails to load is cl_sldwks2sm.dll.

Best Answer

This error message caused by a missing DLL in the MATLAB installation.

To fix the issue, place the attached file "xerces-c_3_1.dll" into your MATLABROOT\bin\win64 folder, where MATLABROOT is the folder where MATLAB is installed.

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MATLAB: Does MATLAB R2017a crash when I save and reload a Simulink.MDLInfo object

This is a bug in MATLAB R2017a (and earlier) that was fixed in R2017b.

To work around the issue, convert the object to a struct before saving it:

mdl_info = struct(Simulink.MDLInfo('vdp.slx'));
save('test.mat');
load('test.mat');
mdl_info

MATLAB: Eigs Performance SA vs. SM

The reason is that eigs uses very different algorithms for the two versions. Here's what each algorithm does

'lm': This is the simplest case: eigs does a smarter version of the power iteration, which is just

for i=1:N
    x = A*x;
    x = x / norm(x);
end

The trick is that the component of x pointing in the direction of the largest eigenvector will grow. The smarter algorithm used in eigs keeps the vectors x in memory, and returns the best linear combination of vectors instead.

'sm', numerical shift sigma: This algorithm uses the shift-and-invert method, which consists in doing (A - simga*I)\x instead of A*x. For 'sm', sigma = 0.

'sa', 'la', 'sr', 'lr', 'si', 'li': These methods do not solve a linear system with A. Instead, they use the power iteration, but choose the linear combination of all vectors that best satisfies the condition given by the input flag.

For your symmetric positive definite problem, the algorithm used in 'sm' will often have faster convergence, while the algorithm used in 'sa' does not require factorization of the matrix A. Which is faster depends on the problem, my personal favorite would be 'sm'.

Best Answer

Related Solutions

MATLAB: Does MATLAB R2017a crash when I save and reload a Simulink.MDLInfo object

MATLAB: Eigs Performance SA vs. SM

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