LTRD — Minimize gradient using full Hessian.

LTRD

When LTRD is used, the gradient norm will be minimized using the full Newton algorithm.
Additionally, a full Hessian is computed at each step making LTRD the most reliable (and
expensive) method for gradient minimization. LTRD can also be used with OPTMAX=0 to build and diagonalize a
Hessian matrix in a given subset of internal coordinates. Thermodynamic quantities may also
be computed from the results of an LTRD calculation. See
Chapter 4, *Computational Procedures* for a more complete
discussion about the LTRD
and NEWTON quadratic optimization methods.

The use of the regular AMPAC keyword LTRD along with
GANNEAL,
TSANNEAL, or
MANNEAL causes the slower
but more reliable LTRD algorithm to be used in the critical point searching procedure. See
Chapter 13, *Simulated Annealing* for a more complete
discussion about the
effect of using LTRD or NEWTON in simulated annealing calculations.

`optimization/opt_ltrd.dat`

):am1 rhf singlet bonds t=auto ltrd grad inertial Conrotary cyclization TS LTRD on TS, INERTIAL C 0.000000 0 0.000000 0 0.000000 0 0 0 0 C 1.368591 1 0.000000 0 0.000000 0 1 0 0 C 1.467760 1 101.846287 1 0.000000 0 2 1 0 C 1.467760 1 105.846287 1 -18.651781 1 1 2 3 H 1.088208 1 133.192008 1 157.464541 1 1 2 3 H 1.088208 1 125.192008 1 145.464541 1 2 1 4 H 1.096007 1 117.078638 1 133.669157 1 4 1 2 H 1.095904 1 124.139890 1 139.767746 1 3 2 1 H 1.098257 1 124.343753 1 -63.184369 1 4 1 2 H 1.098289 1 117.265822 1 -67.054326 1 3 2 1 0 0.000000 0 0.000000 0 0.000000 0 0 0 0

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