On methods for solving nonlinear semidefinite optimization problems
Jie Sun
The nonlinear semidefinite optimization problem arises from applications in system control, structural design, financial management, and other fields. However, much work is yet to be done to effectively solve this problem. We introduce some new theoretical and algorithmic development in this field. In particular, we discuss first and second-order algorithms that appear to be promising, which include the alternating direction method, the augmented Lagrangian method, and the smoothing Newton method. Convergence theorems are presented and preliminary numerical results are reported.
keywords: Alternating direction method augmented Lagrangian method semismooth Newton method.
A smoothing Newton algorithm for mathematical programs with complementarity constraints
Zheng-Hai Huang Jie Sun
We propose a smoothing Newton algorithm for solving mathematical programs with complementarity constraints (MPCCs). Under some reasonable conditions, the proposed algorithm is shown to be globally convergent and to generate a $B$-stationary point of the MPCC. Preliminary numerical results on some MacMPEC problems are reported.
keywords: global convergence. mathematical program with complementarity constraints B-stationary point smoothing algorithm
Designing the distribution network for an integrated supply chain
Jia Shu Jie Sun
We consider an integrated distribution network design problem in which all the retailers face uncertain demand. The risk-pooling benefit is achieved by allowing some of the retailers to operate as distribution centers (DCs) with commitment in service level. The target is to minimize the expected total cost resulted from the DC location, transportation, and inventory. We formulate it as a two-stage nonlinear discrete stochastic optimization problem. The first stage decides which retailers to be selected as DCs and the second stage deals with the costs of DC-retailer assignment, transportation, and inventory. In the literature, the similar models require the demands of all retailers in each scenario to have their variances identically proportional to their means. In this paper, we remove this restriction. We reformulate the problem as a set-covering model and solve it by a column generation approach. With a variable fixing technique, we are able to efficiently solve problems of moderate-size (up to one hundred retailers and nine scenarios).
keywords: supply chain design. set covering Column generation
A class of two-stage distributionally robust games
Bin Li Jie Sun Honglei Xu Min Zhang

An $ N $-person noncooperative game under uncertainty is analyzed, in which each player solves a two-stage distributionally robust optimization problem that depends on a random vector as well as on other players' decisions. Particularly, a special case is considered, where the players' optimization problems are linear at both stages, and it is shown that the Nash equilibrium of this game can be obtained by solving a conic linear variational inequality problem.

keywords: Conic optimization duality game with uncertainty
Inertial accelerated algorithms for solving a split feasibility problem
Yazheng Dang Jie Sun Honglei Xu

Inspired by the inertial proximal algorithms for finding a zero of a maximal monotone operator, in this paper, we propose two inertial accelerated algorithms to solve the split feasibility problem. One is an inertial relaxed-CQ algorithm constructed by applying inertial technique to a relaxed-CQ algorithm, the other is a modified inertial relaxed-CQ algorithm which combines the KM method with the inertial relaxed-CQ algorithm. We prove their asymptotical convergence under some suitable conditions. Numerical results are reported to show the effectiveness of the proposed algorithms.

keywords: Asymptotical convergence inertial technique nonsmooth analysis split feasibility problem subdifferential
Analysis of optimal boundary control for a three-dimensional reaction-diffusion system
Wanli Yang Jie Sun Su Zhang

This paper is concerned with optimal boundary control of a three dimensional reaction-diffusion system in a more general form than what has been presented in the literature. The state equations are analyzed and the optimal control problem is investigated. Necessary and sufficient optimality conditions are derived. The model is widely applicable due to its generality. Some examples in applications are discussed.

keywords: Optimal control reaction-diffusion system optimality conditions
Convergence analysis of a parallel projection algorithm for solving convex feasibility problems
Yazheng Dang Fanwen Meng Jie Sun
The convex feasibility problem (CFP) is a classical problem in nonlinear analysis. In this paper, we propose an inertial parallel projection algorithm for solving CFP. Different from the previous algorithms, the proposed method introduces a sequence of parameters and uses the information of last two iterations at each step. To prove its convergence in a simple way, we transform the parallel algorithm to a sequential one in a constructed product space. Preliminary experiments are conducted to demonstrate that the proposed approach converges faster than the general extrapolated algorithms.
keywords: asymptotic convergence. Convex feasible problem inertial parallel projection algorithm
A weak condition for global stability of delayed neural networks
Ricai Luo Honglei Xu Wu-Sheng Wang Jie Sun Wei Xu
The classical analysis of asymptotical and exponential stability of neural networks needs assumptions on the existence of a positive Lyapunov function $V$ and on the strict negativity of the function $dV/dt$, which often come as a result of boundedness or uniformly almost periodicity of the activation functions. In this paper, we investigate the asymptotical stability problem of Hopfield neural networks with time delays under weaker conditions. By constructing a suitable Lyapunov function, sufficient conditions are derived to guarantee global asymptotical stability and exponential stability of the equilibrium of the system. These conditions do not require the strict negativity of $dV/dt $, nor do they require that the activation functions to be bounded or uniformly almost periodic.
keywords: exponential stability time delays negative semi-definite Asymptotical stability neural networks.
A class of doubly degenerate parabolic equations with periodic sources
Jiebao Sun Boying Wu Jing Li Dazhi Zhang
In this paper, we investigate a class of doubly degenerate parabolic equations with periodic sources subject to homogeneous Dirichlet boundary conditions. By means of the theory of Leray-Schauder degree, we establish the existence of non-trivial nonnegative periodic solutions. The key step is how to establish the uniform bound estimate of approximate solutions, for this purpose we will make use of Moser iteration and some results of the eigenvalue problem for the $p$-Laplacian equation.
keywords: Periodic solutions Doubly degenerate Leray-Schauder degree. Moser iteration

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