st: mata moptimize with Nelder-Mead option: why does it care about Hessian?

["Prieger, James" <James.Prieger@pepperdine.edu>]

Does anyone have experience using moptimize in Mata with technique "nm"
(Nelder-Mead)?  I am trying to use it, but upon execution get the error
message

"Hessian is not negative semidefinite"

Why would moptimize care about the Hessian if I'm using Nelder-Mead?  I
thought the point of the simplex method was for it to work when the
likelihood function isn't continuous or differentiable.  Does
evaluatortype d0 still try to find first and second derivatives anyway?

 

Code excerpt:

mata:
M = moptimize_init()
moptimize_init_evaluator(M, &LLiklRenApp())
moptimize_init_evaluatortype(M, "d0")
moptimize_init_depvar(M, 1, "y c0 c1 c2 c3 z0 z1 z2 z3 a g tau d") //
yes, this is a messy problem!
moptimize_init_depvar(M, 2, "")
moptimize_init_eq_indepvars(M, 1, "x")
moptimize_init_eq_indepvars(M, 2, "")
moptimize_init_eq_indepvars(M, 3, "")
moptimize_init_technique(M, "nm")
delta = J(1,4,.1)
moptimize_init_nmsimplexdeltas(M, delta)
moptimize(M)
end

 

James Prieger
Associate Professor
Pepperdine University School of Public Policy

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