Penlty Function Algorithm Of Nonlinear Programming

Nonlinear constrained optimization problems are the most challenged subjects in mathematical programming. It's very important to seize the method of nonlinear constrained optimization problems. Recently, student have advanced many new methods to solve it , such as penalty functions, feasible point method, multiplier method and sequential quadratic programming. However, the amount of calculation, the speed of the convergence and the sensitivity of parameters of these algorithms are less encouraged. In the present paper, we investigated how to apply the algorithm to the optimization problems with exact penalty function with two-parameter after we apply a new theorem of penalty function.Firstly, we introduced the research condition of the nonlinear constrained optimization problems. Secondly, after applied a new theorem of penalty function, we discussed the property of the penalty function to solve nonlinear constrained optimization problems. ON this base, we constructed a series of exact penalty function with two-parameters , discussed the theorem and applied a theorem of solving nonlinear constrained optimization problems.Finally, integrated with the theorem of unconstrained optimization problems, we applied a new Quasi Newton Algorithm of penalty function with two-parameter. Compare to the traditional algorithm to slave the nonlinear constrained optimization problems, the new algorithm is more excellent in the speed of the convergence and the astringency of solve.Main fruit is building a model of nonlinear constrained optimization problems with exact penalty function with two-parameters, applying a new Quasi Newton Algorithm to solve.This text has the important theory meaning for the solve the nonlinear constrained optimization problems.