Existence and Lagrangian duality for maximizations of set-valued functions
Journal of Optimization Theory and Applications
Optimality conditions for maximizations of set-valued functions
Journal of Optimization Theory and Applications
Contingent derivatives of set-valued maps and applications to vector optimization
Mathematical Programming: Series A and B
Asymptotic analysis for penalty and barrier methods in convex and linear programming
Mathematics of Operations Research
Characterizations of the nonemptiness and compactness of solution sets in convex vector optimization
Journal of Optimization Theory and Applications
A Nonlinear Lagrangian Approach to Constrained Optimization Problems
SIAM Journal on Optimization
The Lagrange Multiplier Rule in Set-Valued Optimization
SIAM Journal on Optimization
Generalized Levitin--Polyak Well-Posedness in Constrained Optimization
SIAM Journal on Optimization
Calmness and Exact Penalization in Vector Optimization with Cone Constraints
Computational Optimization and Applications
Journal of Global Optimization
Levitin---Polyak well-posedness of constrained vector optimization problems
Journal of Global Optimization
Handbook of Multicriteria Analysis
Handbook of Multicriteria Analysis
Journal of Global Optimization
Journal of Global Optimization
Convergence of a class of penalty methods for constrained scalar set-valued optimization
Journal of Global Optimization
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In this paper, we first derive several characterizations of the nonemptiness and compactness for the solution set of a convex scalar set-valued optimization problem (with or without cone constraints) in which the decision space is finite-dimensional. The characterizations are expressed in terms of the coercivity of some scalar set-valued maps and the well-posedness of the set-valued optimization problem, respectively. Then we investigate characterizations of the nonemptiness and compactness for the weakly efficient solution set of a convex vector set-valued optimization problem (with or without cone constraints) in which the objective space is a normed space ordered by a nontrivial, closed and convex cone with nonempty interior and the decision space is finite-dimensional. We establish that the nonemptiness and compactness for the weakly efficient solution set of a convex vector set-valued optimization problem (with or without cone constraints) can be exactly characterized as those of a family of linearly scalarized convex set-valued optimization problems and the well-posedness of the original problem.