Complete Characterizations of Global Optimality for Problems Involving the Pointwise Minimum of Sublinear Functions
Copyright policy )Complete Characterizations of Global Optimality for Problems Involving the Pointwise Minimum of Sublinear Functions
Summary: Necessary and sufficient global optimality conditions are presented for certain non-convex minimization problems subject to inequality constraints that are expressed as the pointwise minimum and sublinear (MSL) functions. A generalized Farkas lemma for inequality systems with MSL functions plays a crucial role in presenting the conditions in dual forms. Applications to certain multiplicative sublinear programming problems and fractional programming problems are also given.
Programming in abstract spaces, non-convex minimization, global optimality conditions, Nonlinear programming, generalized Farkas lemma, Nonsmooth analysis, Fractional programming, \(\varepsilon\)-subdifferential
Programming in abstract spaces, non-convex minimization, global optimality conditions, Nonlinear programming, generalized Farkas lemma, Nonsmooth analysis, Fractional programming, \(\varepsilon\)-subdifferential
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