Optimality and saddle point theorem for robust approximation solutions for non-smooth multi-objective optimization
With the help of strong convex functions,a generalized strong pseudo-quasi-convex function is defined.Under the assumptions of generalized strong convexity and Slater constraints,non-smooth multi-objective optimi-zation problems with uncertain parameters are studied,and feasible solutions to the optimality multi-objective optimization problem to become a robust(weak)sufficient condition and a robust ε-quasi-weak efficient solution are given.By establishing the saddle point theorem,the equivalence between the weak saddle points and the original optimized ε-quasi-weak effective solution is obtained.The optimality condition and saddle point condition of multi-objective robust optimization problem are generalized under the weaker generalized convexity.
multi-objective optimizationoptimality conditiongeneralized strong convex functionssaddle point
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维普
