The minimax exact penalty fuzzy function method for solving convex nonsmooth optimization problems with fuzzy objective functions

Optimization problems under uncertainty has achieved much attention because of inaccurate or incomplete data that often appear in real-world applications. In order to address this challenging issue, several types methodologies have been developed optimization theory and one them is fuzzy optimization. paper, an attempt taken use the minimax exact penalty function method for solving convex nondifferentiable with objective functions both inequality equality constraints. The most important property methods, is, exactness penalization, defined analyzed if applicable a problem function. It proved (weak) KKT point considered (weakly) nondominated solution its associated penalized constructed used approach. Further, conditions are also derived which there equivalence between solutions aforesaid problems. These results established assumption involved convex. algorithm by using proposed convergence established.