(1) ran three models: the first a "general model", the second and third models constrained a (1 x 4) vector of means for one group to equal the (1 x 4) vector of means for another group. the second model did this using an MxAlgebra object while the third used an MxConstraint object.
(2) all models converged with an NPSOL inform value of 0.
(3) gradient and hessian were perfectly fine for models 1 and 2 but not for model 3. largest gradient element was -40 and the norm of the gradient was 5900. the search direction at convergence (inverse(hess) %*% gradient) gave very large elements. the calculated hessian was a (1 x 1) matrix with element NA.
(4) yet, the second and third models gave the same function value and the same parameter estimates.
(5) suspect something funky with the gradient and/or hessian returned with the third model.
belatedly, noticed that i could not attach a *.zip file. go to
to download the R objects.