clpBNR_toolkit:local

local_minima(+ObjF) is semidet

Succeeds if the value of objective function ObjF can be constrained to be a local minimum, i.e, it's "slope" is 0 in every dimension; otherwise fails. This requires that a partial derivative of ObjF exists for each variable. local_minima should be executed prior to a call to clpBNR:global_minimum using the same objective function, e.g.,

?- X::real(0,10), OF=X**3-6*X**2+9*X+6, local_minima(OF), global_minimum(OF,Z).
OF = X**3-6*X**2+9*X+6,
X:: 3.00000000000000...,
Z:: 6.000000000000... .

Using any local optima predicate can significantly improve performance compared to searching for global optima (clpBNR:global_*) without local constraints.

See also: - clpBNR:local_minima/2