The expected discounted penalty function under a renewal risk model with stochastic income

In this paper, we consider a renewal risk model with stochastic premiums income. We assume that the premium number process and the claim number process are a Poisson process and a generalized Erlang (n) processes, respectively. When the individual stochastic premium sizes are exponentially distributed, the Laplace transform and a defective renewal equation for the Gerber-Shiu discounted penalty function are obtained. Furthermore, the discounted joint distribution of the surplus just before ruin and the deficit at ruin is given. When the claim size distributions belong to the rational family, the explicit expression of the Gerber-Shiu discounted penalty function is derived. Finally, a specific example is provided.     

Gradient differences and bilateral barters

Exchange is modelled here as iterated bilateral barters, each fairly myopic and driven merely by gradient differences. Under weak conditions, repeated transactions carry the economy to market equilibrium, supported by clearing prices. A main feature is that agents, in the interim, are allowed non-admissible, possibly speculative, but sharply penalized positions.     

Lvy运动干扰的经典风险模型的Gerber-Shiu函数

通过一个弱收敛方法,本文首次以拉普拉斯变换的形式给出α-稳定Levy运动干扰的经典风险模型的Gerber-Shiu期望折扣惩罚函数(G-S函数).用同样的方法,也获得了这个风险模型的最终破产概率作为本文结果的补充.作为检验,这个风险过程的最终破产概率实际上是G-S函数的特殊情形.     

马氏调制费率的带干扰风险模型

考虑了一类具有马氏调制的带干扰连续时间风险模型,得到了该模型下其条件Gerber-Shiu折现罚金函数所满足的积分方程,Laplace变换及渐近解.在两状态情形下,当索赔额的分布为有理数情况时得到了条件Gerber-Shiu折现罚金函数的具体表达式并给出了数值例子     

连续体结构拓扑优化的一种改进变密度法及其应用

针对连续体结构拓扑优化设计变密度方法SIMP和RAMP,因惩罚函数选取的不合理而导致拓扑结构形式不甚合理的问题,本文提出了一种新的惩罚函数,并基于此函数导出了相应的迭代设计公式,几个典型考题的数值结果,说明了方法的可行性和有效性.     

一个解非线性0-1整数规划问题基于罚函数的混合粒子群优化算法

利用罚函数思想把非线性0-1整数规划问题转化为无约束最优化问题,然后把粒子群优化和罚函数方法结合构造出一个基于罚函数的混合粒子群优化算法,数值结果表明所提出的算法是有效的.     

材料交界面上连续应力分析的改进拉氏乘子法

引进材料界面的节理单元,以其刚度参数为罚参数,利用代表界面接触应力的拉氏乘子,通过迭代求解得到界面上的应力,为计算粘结在一起的不同材料交界面上的应力提供了一种非常有效的方法。     

Inner Approximation Method for a Reverse Convex Programming Problem

In this paper, we consider a reverse convex programming problem constrained by a convex set and a reverse convex set, which is defined by the complement of the interior of a compact convex set X. We propose an inner approximation method to solve the problem in the case where X is not necessarily a polytope. The algorithm utilizes an inner approximation of X by a sequence of polytopes to generate relaxed problems. It is shown that every accumulation point of the sequence of optimal solutions of the relaxed problems is an optimal solution of the original problem.     

A Quasi-Newton Quadratic Penalty Method for Minimization Subject to Nonlinear Equality Constraints

We present a modified quadratic penalty function method for equality constrained optimization problems. The pivotal feature of our algorithm is that at every iterate we invoke a special change of variables to improve the ability of the algorithm to follow the constraint level sets. This change of variables gives rise to a suitable block diagonal approximation to the Hessian which is then used to construct a quasi-Newton method. We show that the complete algorithm is globally convergent. Preliminary computational results are reported.