نتایج جستجو برای: Sufficient global optimality conditions
تعداد نتایج: 1400953 فیلتر نتایج به سال:
in this paper, some kkt type sufficient global optimality conditions for general mixed integer nonlinear programming problems with equality and inequality constraints (minpp) are established. we achieve this by employing a lagrange function for minpp. in addition, verifiable sufficient global optimality conditions for general mixed integer quadratic programming problems are der...
In this paper, some KKT type sufficient global optimality conditions for general mixed integer nonlinear programming problems with equality and inequality constraints (MINPP) are established. We achieve this by employing a Lagrange function for MINPP. In addition, verifiable sufficient global optimality conditions for general mixed integer quadratic programming problems are der...
* Correspondence: [email protected] Department of Mathematics, Yibin University, Yibin, Sichuan 644007, China Full list of author information is available at the end of the article Abstract In this article, by using the Lagrangian function, we investigate the sufficient global optimality conditions for a class of semi-definite optimization problems, where the objective function are general non...
In this paper, we establish global optimality conditions for quadratic optimization problems with quadratic equality and bivalent constraints. We first present a necessary and sufficient condition for a global minimizer of quadratic optimization problems with quadratic equality and bivalent constraints. Then, we examine situations where this optimality condition is equivalent to checking the po...
In this paper we establish necessary as well as sufficient conditions for a given feasible point to be a global minimizer of smooth minimization problems with mixed variables. These problems, for instance, cover box constrained smooth minimization problems and bivalent optimization problems. In particular, our results provide necessary global optimality conditions for difference convex minimiza...
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