Partial Pricing Rule Simplex Method with Deficient Basis

A new partial pricing column rule is proposed to the basis-deficiency-allowing simplex method developed by Pan.Computational results obtained with a set of small problems and a set of standard NETLIB problems show its promise of success.     

Black-Scholes期权定价公式推广

在Black-Scholes期权定价模型的基础上,进一步考虑标的资产受多个跳跃源影响的情况,用含有多维Poisson过程的Ito-Skorohod随机微分方程描述标的资产价格的动态运动,应用等价鞅测度变换方法导出一般形式的欧式期权定价公式,并讨论了利率,波动率不是常数情况下的拓广形式.     

广义Black-Scholes模型期权定价新方法--保险精算方法

利用公平保费原则和价格过程的实际概率测度推广了Mogens Bladt和Tina Hviid Rydberg的结果．在无中间红利和有中间红利两种情况下，把Black-Scholes模型推广到无风险资产(债券或银行存款)具有时间相依的利率和风险资产(股票)也具有时间相依的连续复利预期收益率和波动率的情况，在此情况下获得了欧式期权的精确定价公式以及买权与卖权之间的平价关系．给出了风险资产(股票)具有随机连续复利预期收益率和随机波动率的广义Black-Scholes模型的期权定价的一般方法．利用保险精算方法给出了股票价格遵循广义Ornstein-Uhlenback过程模型的欧式期权的精确定价公式和买权和卖权之间的平价关系．     

A General Zero-Knowledge Scheme

There is a great similarity between the zero-knowledge proof of quadratic residuocity presented by Goldwasser-Micali-Rackoff and the graph isomorphism proof presented by Goldreich-Micali-Wigderson. There is also a resemblance between the zero-knowledge proofs of Fiat-Shamir, Chaum-Evertse-van de Graaf, Beth and Guillou-Quisquater. A similar observation holds for zero-knowledge proofs based on encryption: the 3-colourability proofs and the Hamiltonian-circuit proofs of Blum and Goldreich-Micali-Wigderson, and the Brassard-Chaum-Crepeau proof for SAT. Feige, Fiat and Shamir introduced the concept of zero-knowledge proofs of knowledge. In this paper we present a general zero-knowledge scheme which unifies all these Arthur-Merlin proofs.     

A Modified Bisection Simplex Method for Linear Programming

1.IntroductionThebisectionmethod,proposedinanearlierpaperbytheauth..[7]texploitsinformationoftheoptimalvaluetospeedupthesolutionprocess-However,themethodrequiresabracketontheoptimalvalueaspartofitsinput,anditspromisinglygoodperformancedependsonwhetherasuitablebracketisavailable;itmayevenfaltosolveaproblemiftheinitialintervalprovideddoesnotcontaintheoptimalvalueactuallyInthispaper,themethodismodifiedformoregeneralpurposeuse-Organizedinanalternativesimplerform,thenewversionnolongerneed8anybrathe…     

关于改进单纯形法的一条注记

指出并修正了文献［１］中改进单纯形法的一处错误。     

A New Steepest Edge Approximation for the Simplex Method for Linear Programming

The purpose of this paper is to present a new steepest edge (SE) approximation scheme for the simplex method. The major advantages are its simplicity of recurrences and implementation, low computational overhead (compared to both the exact SE method and the DEVEX approximation scheme), and surprisingly good performance.The paper contains a brief account of the exact SE algorithm, the new recurrences developed in the same framework and some discussion on the possible reasons for the method's apparent success. Finally, numerical experiments are presented to assess the practical value of the method. The results are very promising.     

A Piecewise Linear Dual Phase-1 Algorithm for the Simplex Method

A dual phase-1 algorithm for the simplex method that handles all types of variables is presented. In each iteration it maximizes a piecewise linear function of dual infeasibilities in order to make the largest possible step towards dual feasibility with a selected outgoing variable. The algorithm can be viewed as a generalization of traditional phase-1 procedures. It is based on the multiple use of the expensively computed pivot row. By small amount of extra work per iteration, the progress it can make is equivalent to many iterations of the traditional method. While this is its most important feature, it possesses some additional favorable properties, namely, it can be efficient in coping with degeneracy and numerical difficulties. Both theoretical and computational issues are addressed. Some computational experience is also reported which shows that the potentials of the method can materialize on real world problems.     

Coupling the Gradient Method with a General Exterior Penalization Scheme for Convex Minimization

In this paper, we propose and analyze an algorithm that couples the gradient method with a general exterior penalization scheme for constrained or hierarchical minimization of convex functions in Hilbert spaces. We prove that a proper but simple choice of the step sizes and penalization parameters guarantees the convergence of the algorithm to solutions for the optimization problem. We also establish robustness and stability results that account for numerical approximation errors, discuss implementation issues and provide examples in finite and infinite dimension.     

A General Scheme for Shape Preserving Planar Interpolating Curves

This paper describes the application of the so-called Abstract Schemes (AS) for the construction of shape preserving interpolating planar curves. The basic idea behind AS is given by observing that when we interpolate some data points by a spline, we can dispose of several free parameters d ₀,d ₁,...,d _N (d _i R ^q ), which are associated with the knots. If we now express shape constraints as conditions relative to each interval between two knots, they can be rewritten as a sequences of inclusion conditions: ({d} _i ,d _i+1)D _i R ^2q , where the sets D _i are the corresponding feasible domains. In this setting the problems of existence, construction and selection of an optimal solution can be studied with the help of Set Theory in a general way. The method is then applied for the construction of shape preserving, planar interpolating curves.     

广义交换期权定价

基于风险中性(等价鞅测度)定价理论和经典的Black-Scholes市场环境,我们给出了更一般情形下的欧式交换期权(ExchangeOption)封闭形式的解析定价公式,进而得出了欧式交换期权的价格公式、Black-Scholes期权定价公式.     

A General Method for Building Reflections

We establish a general method for generating reflections between categories. We then apply our technique to generate adjunctions starting from geometric morphisms between Grothendieck toposes; as particular cases, we recover various well-known Stone-type adjunctions and establish several new ones.     

The Simplex and the Dual Method for Quadratic Programming

The paper presents a straightforward generalization of the Simplex and the dual method for linear programming to the case of convex quadratic programming. The two algorithms, called the Simplex and the dual method for quadratic programming, are applicable when the matrix of the quadratic part of the objective function, in case this function is to be maximized, is negative definite, negative semi-definite or zero; in the last case the two methods are equivalent to an application of the similar methods for linear programming. The paper gives an exposition of the methods as well as examples and interpretations. The relations with linear programming methods are considered and some starting procedures in case no initial feasible solution is available are presented.     

A General Approximation Scheme for Attractors of Abstract Parabolic Problems

We consider semilinear problems of the form u′ = Au + f(u), where A generates an exponentially decaying compact analytic C ₀-semigroup in a Banach space E, f:E ^α → E is differentiable globally Lipschitz and bounded (E ^α = D((?A)^α) with the graph norm). Under a very general approximation scheme, we prove that attractors for such problems behave upper semicontinuously. If all equilibrium points are hyperbolic, then there is an odd number of them. If, in addition, all global solutions converge as t → ±∞, then the attractors behave lower semicontinuously. This general approximation scheme includes finite element method, projection and finite difference methods. The main assumption on the approximation is the compact convergence of resolvents, which may be applied to many other problems not related to discretization.     

基于最钝角规则的亏基对偶单纯形Ⅰ阶段算法

对偶单纯形算法或原始对偶单纯形算法都需要一个初始对偶可行基．就此目的而言，基于最钝角行主元规则的对偶Ⅰ阶段算法非常有效[15].本文将其思想应用于亏基情形，建立一个不含比值检验的新的亏基对偶Ⅰ价段算法．初步的数值实验表明，该算法可在总体上减少运行时间和迭代次数，极具竞争性．     

Primal-Dual Simplex Method for Multiobjective Linear Programming

We develop a primal-dual simplex algorithm for multicriteria linear programming. It is based on the scalarization theorem of Pareto optimal solutions of multicriteria linear programs and the single objective primal-dual simplex algorithm. We illustrate the algorithm by an example, present some numerical results, give some further details on special cases and point out future research. The paper was written during a visit of the first author to the University of Sevilla financed by a grant of the Andalusian Consejería de Educación. The research of the first author was partially supported by University of Auckland Grant 3602178/9275. The research of the second and third authors was partially financed by Spanish Grants BFM2001-2378, BFM2001-4028, MTM2004-0909 and HA2003-0121. We thank Anthony Przybylski for the implementation and making his results available. We thank the anonymous referees, whose comments have helped us to improve the presentation of the paper.     

一种使用单纯形法优化的粒子群算法

根据单纯形法和粒子群算法的各自特点,提出了一种使用单纯形法优化的粒子群算法,算法利用单纯形法来对粒子群算法的初始值进行处理.数值实验表明,优化后的粒子群算法具有更好的的寻优能力.     

修正单纯形法的计算量的注记

对文献［１］、［２］指出的修正单纯形法的计算量提出了异议,并给出了修正单纯形法应有的计算量。     

基于单纯形方法的双层线性规划全局优化算法

通过分析双层线性规划可行域的结构特征和全局最优解在约束域的极点上达到这一特性,对单纯形方法中进基变量的选取法则进行适当修改后,给出了一个求解双层线性规划局部最优解方法,然后引进上层目标函数对应的一种割平面约束来修正当前局部最优解,直到求得双层线性规划的全局最优解.提出的算法具有全局收敛性,并通过算例说明了算法的求解过程.