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Hi, I have been using CGAl for quadratic optimization with good success, yet I
encounter a problem that often causes CGAl to crash.

In the quadratic optimizer, I specify a matrix Q = transpose( V ) * V, where V
is rather long than tall, hence Q has a low rank. In theory, Q is positive
semi-definite, but in practice small rounding errors cause it to have barely
negative eigenvalues and CGAL understandably complains on assertion that the
matrix is not a proper semi-definite.

Is there a way to deal with it from within CGAl, and if not, what would be the
most effective way to alter the input in order for CGAL to accept?

Anyone's help or ideas are more than welcome

Thanks

Vladimir