On generalised convex mathematical programming 论文
1992The Journal of the Australian Mathematical Society Series B Applied Mathematics引用 234
Optimization and Variational AnalysisOptimization and Mathematical ProgrammingAdvanced Optimization Algorithms Research
摘要
Abstract The sufficient optimality conditions and duality results have recently been given for the following generalised convex programming problem: where the funtion f and g satisfy for some η: X 0 × X 0 → ℝ n It is shown here that a relaxation defining the above generalised convexity leads to a new class of multi-objective problems which preserves the sufficient optimality and duality results in the scalar case, and avoids the major difficulty of verifying that the inequality holds for the same function η(. , .). Further, this relaxation allows one to treat certain nonlinear multi-objective fractional programming problems and some other classes of nonlinear (composite) problems as special cases.