X. Q. Yang

Non-differentiable second order symmetric duality in mathematical programming with F-convexity☆

Abstract A pair of Wolfe type non-differentiable second order symmetric primal and dual problems in mathematical programming is formulated. The weak and strong duality theorems are then established under second order F-convexity assumptions. Symmetric minimax mixed integer primal and dual problems are also investigated.

EXISTENCE AND DUALITY OF IMPLICIT VECTOR VARIATIONAL PROBLEMS

In this paper, we consider implicit vector variational problems which contain vector equilibrium problems and vector variational inequalities as special cases. The existence of solutions of implicit vector variational problems and vector equilibrium problems have been established. As a special case, we derive some existence results for a solution of vector variational inequalities. We also study the duality of implicit vector variational problems and discuss the relationship between solutions of dual and primal problems. Our results on duality contains known results in the literature as special cases. *This research was supported by the National Science Council of Taiwan and carried out during the stay of second author at Department of Applied Mathematics, National Sun Yat-sen University, Kaohsiung, Taiwan.

Multiobjective second-order symmetric duality with F-convexity☆

We suggest a pair of second-order symmetric dual programs in multiobjective nonlinear programming. For these second-order symmetric dual programs, we prove the weak, strong and converse duality theorems under F-convexity conditions.

The System of Vector Quasi-Equilibrium Problems with Applications

In this paper, we consider the system of vector quasi-equilibrium problems with or without involving Φ-condensing maps and prove the existence of its solution. Consequently, we get existence results for a solution to the system of vector quasi-variational-like inequalities. We also prove the equivalence between the system of vector quasi-variational-like inequalities and the Debreu type equilibrium problem for vector-valued functions. As an application, we derive some existence results for a solution to the Debreu type equilibrium problem for vector-valued functions.

A Sequential Smooth Penalization Approach to Mathematical Programs with Complementarity Constraints

In this paper, a mathematical program with complementarity constraints (MPCC) is reformulated as a nonsmooth constrained mathematical program via the Fischer–Burmeister function. Smooth penalty functions are used to treat this nonsmooth constrained program. Under linear independence constraint qualification, and upper level strict complementarity condition, together with some other mild conditions, we prove that the limit point of stationary points satisfying second-order necessary conditions of unconstrained penalized problems is a strongly stationary point, hence a B-stationary point of the original MPCC. Furthermore, this limit point also satisfies a second-order necessary condition of the original MPCC. Numerical results are presented to test the performance of this method.

Smoothing Nonlinear Penalty Functions for Constrained Optimization Problems

Abstract In this article, we discuss a nondifferentiable nonlinear penalty method for an optimization problem with inequality constraints. A smoothing method is proposed for the nonsmooth nonlinear penalty function. Error estimations are obtained among the optimal value of smoothed penalty problem, the optimal value of the nonsmooth nonlinear penalty optimization problem and that of the original constrained optimization problem. We give an algorithm for the constrained optimization problem based on the smoothed nonlinear penalty method and prove the convergence of the algorithm. The efficiency of the smoothed nonlinear penalty method is illustrated with a numerical example.

Quadratic cost flow and the conjugate gradient method

By introducing quadratic penalty terms, a convex non-separable quadratic network program can be reduced to an unconstrained optimization problem whose objective function is a piecewise quadratic and continuously differentiable function. A conjugate gradient method is applied to the reduced problem and its convergence is proved. The computation exploits the special network data structures originated from the network simplex method. This algorithmic framework allows direct extension to multicommodity cost flows. Some preliminary computational results are presented.

Survey on Vector Complementarity Problems

The paper aims at summarizing the main results on Vector Complementarity Problems (VCP), including the existence of a solution and the relations with Vector Variational Inequalities and Vector Optimization Problems. Particular attention will be given to a VCP with a variable domination structure, where the ordering cone depends on the unknown variable.

Levitin-Polyak Well-Posedness of Vector Variational Inequality Problems with Functional Constraints

The Levitin–Polyak well-posedness for a constrained problem guarantees that, for an approximating solution sequence, there is a subsequence which converges to a solution of the problem. In this article, we introduce several types of (generalized) Levitin–Polyak well-posednesses for a vector variational inequality problem with both abstract and functional constraints. Various criteria and characterizations for these types of well-posednesses are given. Relations among these types of well-posednesses are presented.

A Subgradient Method Based on Gradient Sampling for Solving Convex Optimization Problems

Based on the gradient sampling technique, we present a subgradient algorithm to solve the nondifferentiable convex optimization problem with an extended real-valued objective function. A feature of our algorithm is the approximation of subgradient at a point via random sampling of (relative) gradients at nearby points, and then taking convex combinations of these (relative) gradients. We prove that our algorithm converges to an optimal solution with probability 1. Numerical results demonstrate that our algorithm performs favorably compared with existing subgradient algorithms on applications considered.