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In this notebook we demonstrate the point of the augmented Lagrangian with a \
very simple example:
\tmin x^2 subject to x = 3.\
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Now minimizer exactly at 3!   But had to pay for it with \
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Cell["\<\
Just Lagrangian.  Exact minimizer (w.r.t. x variable) at 3!  (We know \
Lagrangian has zero gradient w.r.t. both x and lambda at minimizer.)  What if \
have approximation to lambda?\
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