matrix - what is the efficient way of implementing CholeskySolve in eigen without using intermediate matrices or buffers? -

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i tried follows

l = l.triangularview<lower>(); x1 = (l*l.transpose()).llt().solve(y1);

where l,y1 input matrices , x1 output matrix.output came expected,but in case l matrix changes after execution of first statement.
don't want change l matrix.
suggestions.

you have llt factorization, apply l's inverse twice:

x = l.triangularview<lower>().solve(y); x = l.triangularview<lower>().transpose().solve(x);

no temporary, performed in-place.


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