Developing A Constrained Search Approach For Solving Of Nonlinear Equations
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This research Present a new constrained optimization approach for solving system of non-liner equations. Particular advantages are realized when all of the equations are convex. For Example, a global algorithm for finding the zero of a convex real-valued function of one variable is developed. If the algorithm terminates finitely. Then either the algorithm has computed a zero or determined that none exists: if an infinite sequence is generated, either that sequence converges to a zero or again no zero exists. For Solving n-dimensional convex equations, the constrained optimization algorithm has the capability of determining that the system of equations has on solution. Global convergence of the algorithm is established under weaker conditions than previously known. It is also shown once novelty has led to a new algorithm for solving the linear complementarily Problem.