The solution of a constrained optimization problem is easily solved by linearly approximating a point of interest to a first hypersurface given as a constraint and finding the extreme value of the objective function by moving a point r along a curved line having second-order osculation with the geodetic line of a hypersurface S(r)=c and passing through the point r.

 
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> Reformulation of constraint satisfaction problems for stochastic search

> Method of scoring and automatically qualifying search results based on a pre-defined scoring matrix relating to a knowledge domain of third-parties invoking a rule construction tool to construct scoring rules to answers for questions within the knowledge domain

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