An Exact Minimax Penalty Function Method and Saddle Point Criteria for Nonsmooth Convex Vector Optimization Problems

ABSTRACTIn this article, the vector exact l1 penalty function method used for solving nonconvex nondifferentiable multiobjective programming problems is analyzed. In this method, the vector penalized optimization problem with the vector exact l1 penalty function is defined. Conditions are given guaranteeing the equivalence of the sets of (weak) Pareto optimal solutions of the considered nondifferentiable multiobjective programming problem and of the associated vector penalized optimization problem with the vector exact l1 penalty function. This equivalence is established for nondifferentiable invex vector optimization problems. Some examples of vector optimization problems are presented to illustrate the results established in the article.

In this paper, the classical exact absolute value penalty function method is used for solving a new class of nonconvex nonsmooth optimization problems. Nonconvex nondifferentiable optimization problems with both inequality and equality constraints are considered here, in which not all functions constituting them have the fundamental property of convex functions and most classes of generalized convex functions—namely, a stationary point of such a function is its global minimum. It is proved for such nonconvex optimization problems that there exists a finite threshold of penalty parameters equal to the largest absolute value of a Lagrange multiplier such that, for every penalty parameter exceeding this lower bound, there is the equivalence between an optimal solution in the original constrained minimization problem with ‐invex functions and a minimizer in its associated penalized optimization problem with the exact l1 penalty function. Further, under nondifferentiable ‐invexity assumptions, a characterization of a saddle point of the Lagrange function, defined for the considered constrained optimization problem in terms of minimizers of its associated exact penalized optimization problem with the exact l1 penalty function, is presented.

In this paper, we study a new exact and smooth penalty function forsemi-infinite programming problems with continuous inequalityconstraints. Through this exact penalty function, we can transform asemi-infinite programming problem into an unconstrained optimizationproblem. We find that, under some reasonable conditions when thepenalty parameter is sufficiently large, the local minimizer of thispenalty function is the local minimizer of the primal problem.Moreover, under some mild assumptions, the local exactness propertyis explored. The numerical results demonstrate that it is aneffective and promising approach for solving constrainedsemi-infinite programming problems.

In this paper, we consider a general class of nonlinear mixed discrete programming problems. By introducing continuous variables to replace the discrete variables, the problem is first transformed into an equivalent nonlinear continuous optimization problem subject to original constraints and additional linear and quadratic constraints. Then, an exact penalty function is employed to construct a sequence of unconstrained optimization problems, each of which can be solved effectively by unconstrained optimization techniques, such as conjugate gradient or quasi-Newton methods. It is shown that any local optimal solution of the unconstrained optimization problem is a local optimal solution of the transformed nonlinear constrained continuous optimization problem when the penalty parameter is sufficiently large. Numerical experiments are carried out to test the efficiency of the proposed method.

On an exact penalty function method for nonlinear mixed discrete programming problems and its applications in search engine advertising problems

We introduce the concept of null set in the space of all bounded closed intervals. Based on this concept, we can define two partial orderings according to the substraction and Hukuhara difference between any two bounded closed intervals, which will be used to define the solution concepts of interval-valued optimization problems. On the other hand, we transform the interval-valued optimization problems into the conventional vector optimization problem. Under these settings, we can apply the technique of scalarization to solve this transformed vector optimization problem. Finally, we show that the optimal solution of the scalarized problem is also the optimal solution of the original interval-valued optimization problem.

ABSTRACTThis paper studies a distributed constraint optimization problem for a multi‐agent system with unknown cost functions and asymmetric input dead‐zone. First, the constraint optimization problem is transformed into an unconstrained optimization problem by using the exact penalty method. As only measurements of the cost functions and local inequality constraints are available, a distributed extremum‐seeking algorithm is presented based on an event‐triggered communication scheme to reduce communication frequency. Moreover, to address the impact of input dead‐zones, a dynamic compensation scheme is introduced into the designed algorithm. Due to the dual time‐scale structure of the designed distributed algorithm, singular perturbation techniques are used to achieve semiglobally practically asymptotical convergence of the decision variables of all agents to the optimal solution. Finally, simulations verify the effectiveness of the proposed algorithm.

This paper discusses the optimality conditions for the strong efficient solutions of set-valued optimization problems. Firstly, by using the second-order weak subdifferential of set-valued mappings and the relationship between the strong efficient solutions of set-valued optimization problems and the optimal solutions of composite optimization problems, one establishes the optimality necessary and sufficient conditions of the strong efficient solutions for unconstrained set-valued optimization problems. Meanwhile, by using the second-order weak subdifferential of set- valued mappings, several optimality necessary and sufficient conditions of unconstrained composite optimization problems are obtained under the assumption of generalized cone sub-convexlikeness. Secondly, one introduces a class of saddle points for constrained set-valued optimization problems. Then, several sufficient optimality conditions for strong efficient solutions of constrained set-valued optimization problems are established under the assumptions of generalized cone sub-convexlikeness and saddle points for constrained set-valued optimization problems. Additionally, by using the second-order weak subdifferential of set-valued mappings, the sufficient optimality conditions for strong efficient solutions of constrained set-valued optimization problems are established under the assumption of generalized cone sub-convexlikeness. Finally, two optimality sufficient conditions for the optimal solutions of the constrained composite optimization problem are obtained under the assumption of generalized cone sub-convexlikeness. 主要讨论集值优化问题强有效解的最优性条件。首先，借助于集值映射的二阶弱次微分，利用集合优化问题强有效解与复合优化问题最优解的关系，在广义锥次似凸假设条件下建立了无约束集值优化问题强有效解的最优性必要条件和充分条件。同时，利用集值映射的二阶弱次微分，在广义锥次似凸假设条件下也获得了无约束复合优化问题的几个最优性必要条件和充分条件。其次，引入了约束集值优化问题的一类鞍点，并在广义锥次似凸和约束集值优化问题鞍点的假设条件下，建立了约束集值优化问题强有效解的最优性充分条件。另外，借助于集值映射的二阶弱次微分，在广义锥次似凸假设条件下，建立了约束集值优化问题强有效解的最优性充分条件。最后，在广义锥次似凸假设条件下，获得了约束复合优化问题最优解的2个最优性充分条件。

We use asymptotic analysis to develop finer estimates for the efficient, weak efficient and proper efficient solution sets (and for their asymptotic cones) to convex/quasiconvex vector optimization problems. We also provide a new representation for the efficient solution set without any convexity assumption, and the estimates involve the minima of the linear scalarization of the original vector problem. Some new necessary conditions for a point to be efficient or weak efficient solution for general convex vector optimization problems, as well as for the nonconvex quadratic multiobjective optimization problem, are established.

In this paper, a so-called approximated vector optimization problem associated to a vector optimization problem is considered. The equivalence between the efficient solutions of the approximated vector optimization problem and efficient solutions of the original optimization problem is established.

In this paper, we consider a class of optimal control problems involving continuous control and state inequality constraints where the control is almost smooth. We first employ the control parametrization technique via approximating the control signal by a piecewise linear function. Then, we develop a time scaling transformation procedure for transforming the approximate problem into an equivalent problem that can be solved readily using conventional methods. On this basis, a novel exact penalty function method is constructed by appending penalized constraint violations to the cost function. The gradient formulas and convergent properties ensure that the transformed unconstrained optimal parameter selection problems can be solved by existing optimization algorithms or software packages. Finally, an example is solved showing the effectiveness and applicability of the approach proposed.

A modified Polak–Ribière–Polyak conjugate gradient algorithm for nonsmooth convex programs

Robust identification of nonlinear state-dependent impulsive switched system with switching duration constraints

Generalized properly efficient solutions of a vector optimization problem (VP) are defined in terms of various tangent cones and a generalized directional derivative. We study their basic properties and relationships and show that under certain conditions, a generalized properly efficient solution of (VP), defined by the adjacent cone, is a generalized Kuhn-Tucker properly efficient solution of (VP). Furthermore, using subgradients defined by closed convex tangent cones, we give a necessary optimality condition for a generalized properly efficient solution of (VP) defined by the adjacent cone.

In the last three decades, the theory of variational analysis provides very effective and powerful techniques for studying a wide class of problems arising in nonlinear equations, optimization problems, economics equilibrium, game theory, complementarity problems, and fixed point problems, as well as other branches of mathematics and engineering sciences. So, the thorough study of both theory and methods about variational inequalities will help us to find new techniques for solving the practical problem. Vector optimization problems have received much attention by many authors due to their extensive applications in many fields such as biology, economics, optimal control, and differential inclusions. Because of the importance and active impact of the variational inequality and the vector optimization problem in the nonlinear analysis and optimization, this special issue, focusing on most recent contributions, includes works on variational inequalities, equilibrium problems and nonexpansive mappings, vector optimization problems and generalized convex functions, robust optimization problems, and optimization problems with applications, which are based on a strict international peer review procedure and our original proposal. A brief review of the papers is given under the following four topics.