On a Modified Subgradient Algorithm for Dual Problems via Sharp Augmented Lagrangian*

We study convergence properties of a modified subgradient algorithm, applied to the dual problem defined by the sharp augmented Lagrangian. The primal problem we consider is nonconvex and nondifferentiable, with equality constraints. We obtain primal and dual convergence results, as well as a condition for existence of a dual solution. Using a practical selection of the step-size parameters, we demonstrate the algorithm and its advantages on test problems, including an integer programming and an optimal control problem. *Partially Supported by 2003 UniSA ITEE Small Research Grant Ero2. Supported by CAPES, Brazil, Grant No. 0664-02/2, during her visit to the School of Mathematics and Statistics, UniSA.     

一类广义Bent型S-Box的构造

S-box是密码理论与实践中十分重要的一种装置 ,它的密码性能由其分量函数所决定 .于是 ,选择适当的分量函数来构造 S-box就成了一个重要的研究课题 .在一定意义上 ,Bent函数是最优良的密码函数 .本文通过函数序列半群和置换群来构造其任何非零线性组合为 Bent函数与线性函数之和的函数组 ,从而可由 Bent函数构造出具有高度非线性度和其他良好性状的 S-box     

Non-Euler–Lagrangian Pareto-optimality Conditions for Dynamic Multiple Criterion Decision Problems

In this paper the problem of verifying the Pareto-optimality of a given solution to a dynamic multiple-criterion decision (DMCD) problem is investigated. For this purpose, some new conditions are derived for Pareto-optimality of DMCD problems. In the literature, Pareto-optimality is characterized by means of Euler-Lagrangian differential equations. There exist problems in production and inventory control to which these conditions cannot be applied directly (Song 1997). Thus, it is necessary to explore new conditions for Pareto-optimality of DMCD problems. With some mild assumptions on the objective functionals, we develop necessary and/or sufficient conditions for Pareto-optimality in the sprit of optimization theory. Both linear and non-linear cases are considered.     

Development of a parallel storm surge model

A new parallel storm surge model, the Parallel Environmental Model (PEM), is developed and tested by comparisons with analytic solutions. The PEM is a 2‐D vertically averaged, wetting and drying numerical model and can be operated in explicit, semi‐implicit and fully implicit modes. In the implicit mode, the propagation, Coriolis and bottom friction terms can all be treated implicitly. The advection and diffusion terms are solved with a parallel Eulerian–Lagrangian scheme developed for this study. The model is developed specifically for use on parallel computer systems and will function accordingly in either explicit of implicit modes. Storm boundary conditions are based on a simple exponential decay of pressure from the centre of a storm. The simulated flooding caused by a major Category 5 hurricane making landfall in the Indian River Lagoon, Florida is then presented as an example application of the PEM. Copyright © 2003 John Wiley & Sons, Ltd.     

短脉冲载荷下金属材料的层裂破坏

 本文用点炮加载聚酯薄膜飞片撞击金属靶材料，飞片速度0.714~2.24 km/s，脉冲宽度40~80 ns，击靶压力为2.280~11.952 GPa。在回收的铝、铜和钢靶样品上观察到层裂现象。通过金相分析发现它们的破坏方式与在一般较长脉冲加载下的破坏方式相似，铝和铜以延性方式破坏；钢则以脆性方式破坏。一维和二维数值模拟结果表明通常的几种层裂判据在这个应力、应变和应变率范围内也基本上适用。     

Semidefinite optimization models for limit and shakedown analysis problems involving matrix spreads

Limit and shakedown analysis problems of Computational Mechanics lead to convex optimization problems, characterized by linear objective functions, linear equality constraints and constraints expressing the restrictions imposed by the material strength. It is shown that two important strength criteria, the Mohr–Coulomb and the Tresca criterion, can be represented as systems of semidefinite constraints, leading this way to semidefinite programming problems.     

Primal-dual proximal point algorithm for linearly constrained convex programming problems

A primal-dual version of the proximal point algorithm is developed for linearly constrained convex programming problems. The algorithm is an iterative method to find a saddle point of the Lagrangian of the problem. At each iteration of the algorithm, we compute an approximate saddle point of the Lagrangian function augmented by quadratic proximal terms of both primal and dual variables. Specifically, we first minimize the function with respect to the primal variables and then approximately maximize the resulting function of the dual variables. The merit of this approach exists in the fact that the latter function is differentiable and the maximization of this function is subject to no constraints. We discuss convergence properties of the algorithm and report some numerical results for network flow problems with separable quadratic costs.     

一类Lagrangian对偶问题的零对偶间隙性质及其最优路径的收敛性

针对一般的非线性规划问题,利用某些Lagrange型函数给出了一类Lagrangian对偶问题的一般模型,并证明它与原问题之间存在零对偶间隙．针对具体的一类增广La- grangian对偶问题以及几类由非线性卷积函数构成的Lagrangian对偶问题,详细讨论了零对偶间隙的存在性．进一步,讨论了在最优路径存在的前提下,最优路径的收敛性质．     

An invariance of geometric mean with respect to Lagrangian means

The invariance of the geometric mean G with respect to the Lagrangian mean-type mapping (Lf,Lg), i.e. the equation G○(Lf,Lg)=G, is considered. We show that the functions f and g must be of high class regularity. This fact allows to reduce the problem to a differential equation and determine the second derivatives of the generators f and g.