Higher-order variational set of the benson proper perturbation map in set-valued optimization

In the paper, we study sensitivity analysis in set-valued optimization, a research direction has been attracting much attention of many mathematicians in the world recently. The main derivative used in the paper is higher-order variational set (introduced by Khanh and Tuan in 2008) which is considered as a generalization of the contingent derivative (known as the first and the most popular derivative in set-valued optimization). Firstly, we establish relationships between higher-order variational sets of a given set-valued map and those of its profile (extended by a ordering cone). Then, we give results on higher-order variational set of the Benson proper perturbation map for a kind of set-valued optimization problem, the perturbation map is defined in the objective space. Finally, we apply the obtained results to sensitivity analysis for optimal-value map of a parametrized constrained set-valued optimization problem whose the objective map and constrained maps depends on some parameter. More precisely, some results on sensitivity analysis for parametrized constrained set-valued optimization problem are obtained. The content of the paper gives us more applications of higher-order variational set in set-valued optimization.

Second-Order Composed Contingent Derivative of the Perturbation Map in Multiobjective Optimization

In view of the structural advantage of second-order composed derivatives, the purpose of this paper is to analyze quantitatively the behavior of perturbation maps for the first time by using this concept. First, new concepts of the second-order composed adjacent derivative and the second-order composed lower Dini derivative are introduced. Some relationships among the second-order composed contingent derivative, the second-order composed adjacent derivative and the second-order composed lower Dini derivative are discussed. Second, the relationships between second-order composed lower Dini derivable and Aubin property are provided. Third, by virtue of second-order composed contingent derivatives and the above relationships, some results concerning second-order sensitivity analysis are established without the assumption of the locally Lipschitz property or the locally Hölder continuity. Finally, we give some complete characterizations of second-order composed contingent derivatives of the perturbation maps.

Characterizing the Lagrange multiplier rule in nonconvex set-valued optimization

In this article, by using the notions of contingent derivative, contingent epiderivative and generalized contingent epiderivative, we obtain some characterizations of the Lagrange multiplier rule at points which are not necessarily local minima.

The second-order contingent derivative of generalized perturbation maps

In the paper, we give some remarks on [1]. Then, we modify main results concerning the sum rule of second-order contingent derivatives for set-valued maps and its application to the sensitivity analysis of generalized perturbation maps. The obtained results are new and better than those in [1]. Some examples are proposed to illustrate our results.

Necessary and Sufficient Conditions for Set-Valued Maps with Set Optimization

Optimality conditions are studied for set-valued maps with set optimization. Necessary conditions are given in terms of S-derivative and contingent derivative. Sufficient conditions for the existence of solutions are shown for set-valued maps under generalized quasiconvexity assumptions.

A new method of proof of Filippov’s theorem based on the viability theorem

Filippov’s theorem implies that, given an absolutely continuous function y: [t 0; T] → ℝd and a set-valued map F(t, x) measurable in t and l(t)-Lipschitz in x, for any initial condition x 0, there exists a solution x(·) to the differential inclusion x′(t) ∈ F(t, x(t)) starting from x 0 at the time t 0 and satisfying the estimation $$\left| {x(t) - y(t)} \right| \leqslant r(t) = \left| {x_0 - y(t_0 )} \right|e^{\int_{t_0 }^t {l(s)ds} } + \int_{t_0 }^t \gamma (s)e^{\int_s^t {l(\tau )d\tau } } ds,$$ where the function γ(·) is the estimation of dist(y′(t), F(t, y(t))) ≤ γ(t). Setting P(t) = {x ∈ ℝn: |x −y(t)| ≤ r(t)}, we may formulate the conclusion in Filippov’s theorem as x(t) ∈ P(t). We calculate the contingent derivative DP(t, x)(1) and verify the tangential condition F(t, x) ∩ DP(t, x)(1) ≠ ø. It allows to obtain Filippov’s theorem from a viability result for tubes.