Constraints satisfaction problems (CSPs) aim at solution via
algorithms that search a domain space for goal states. The
solution of which must satisfy all constraints and guarantees
explicit reasoning structure that conveys data about the
problem to the algorithm. Thus, it assigns to an output, a set
of variables that satisfies a set of constraints in its bid to
prune off huge portion of the search space. This study
presents solutions to quadratic functions via David Fletcher
Powell method and stochastic method of optimization. To
aim this purpose, hybrid neural networks are trained using
DFP as a pre-processor to yield approximate solutions to the
quadratic function. A trial solution of the quadratic equation
is written as sum of two parts: (a) first part satisfies the initial
condition for unconstrained optimization using DFP and
hybrids as separate methods to solve a quadratic function;
while (b) second part uses DFP as a pre-processor with
adjustable parameters of for the ANN-TLRN hybrid. Results
show that presented method introduces a closer form to the
analytic solution. These present method is easily extended to
solve a wide range of problems.