The Basis Digraphs of <Emphasis Type="Italic">p</Emphasis>-schemes

It is proved that association schemes with bipartite basis graphs are exactly 2-schemes. This result follows from a characterization of p-schemes for an arbitrary prime p in terms of basis digraphs. Second author work was partially supported by RFFI Grants 07-01-00485, 08-01-00379 and 08-01-00640. First author was visiting the Euler Institute of Mathematics, St. Petersburg, Russia during the time a part of this paper was written and he thanks the Euler Institute for its hospitality     

Mathematical Programs with Vanishing Constraints: Optimality Conditions,Sensitivity, and a Relaxation Method

We consider a class of optimization problems with switch-off/switch-on constraints, which is a relatively new problem model. The specificity of this model is that it contains constraints that are being imposed (switched on) at some points of the feasible region, while being disregarded (switched off) at other points. This seems to be a potentially useful modeling paradigm, that has been shown to be helpful, for example, in optimal topology design. The fact that some constraints “vanish” from the problem at certain points, gave rise to the name of mathematical programs with vanishing constraints (MPVC). It turns out that such problems are usually degenerate at a solution, but are structurally different from the related class of mathematical programs with complementarity constraints (MPCC). In this paper, we first discuss some known first- and second-order necessary optimality conditions for MPVC, giving new very short and direct justifications. We then derive some new special second-order sufficient optimality conditions for these problems and show that, quite remarkably, these conditions are actually equivalent to the classical/standard second-order sufficient conditions in optimization. We also provide a sensitivity analysis for MPVC. Finally, a relaxation method is proposed. For this method, we analyze constraints regularity and boundedness of the Lagrange multipliers in the relaxed subproblems, derive a sufficient condition for local uniqueness of solutions of subproblems, and give convergence estimates. Research of the first author was supported by the Russian Foundation for Basic Research Grants 07-01-00270, 07-01-00416 and 07-01-90102-Mong, and by RF President’s Grant NS-9344.2006.1 for the support of leading scientific schools. The second author was supported in part by CNPq Grants 301508/2005-4, 490200/2005-2 and 550317/2005-8, by PRONEX-Optimization, and by FAPERJ.     

Stochastic optimal control on dividend policies with bankruptcy

ABSTRACT

When a firm is at the edge of bankruptcy, it would endeavour to attract bailouts from governments or financial institutions to cast off bad situation. If this effort fails, then the firm would face to sell off their properties to pay their debts to loaners or shareholders. In this paper, from these two cases of bankruptcy, two optimal dividend policies are considered and analysed, respectively. In the case of unrestricted dividend payment rate, a terminal bankruptcy model with non-zero terminal value is put forward. An analytic solution for the optimal objective function, which maximizes the expected value of total discounted dividends before bankruptcy and the residual value at bankruptcy, is provided and verified. As a significant application, a non-terminal bankruptcy problem with bailouts is considered, an explicit solution and the corresponding control policies are also obtained. In the end, some numerical examples are listed and the influence of the recovery rate on the optimal strategies is also discussed.     

Profile empirical likelihood for parametric and semiparametric models

This paper introduces a profile empirical likelihood and a profile conditionally empirical likelihood to estimate the parameter of interest in the presence of nuisance parameters respectively for the parametric and semiparametric models. It is proven that these methods propose some efficient estimators of parameters of interest in the sense of least-favorable efficiency. Particularly, for the decomposable semiparametric models, an explicit representation for the estimator of parameter of interest is derived from the proposed nonparametric method. These new estimations are different from and more efficient than the existing estimations. Some examples and simulation studies are given to illustrate the theoretical results. The first author is supported by NNSF projects (10371059 and 10171051) of China. The second author is supported by a grant from The Research Grants Council of the Hong Kong Special Administrative Region, China (#HKU7060/04P). The third author is supported by the University Research Committee of the University of Hong Kong and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKU7323/01M).     

Interior proximal point algorithm for linear programs

An interior proximal point algorithm for finding a solution of a linear program is presented. The distinguishing feature of this algorithm is the addition of a quadratic proximal term to the linear objective function. This perturbation has allowed us to obtain solutions with better feasibility. Implementation of this algorithm shows that the algorithms. We also establish global convergence and local linear convergence of the algorithm.This research was supported by National Science Foundation Grants DCR-85-21228 and CCR-87-23091 and by Air Force Office of Scientific Research Grants AFOSR-86-0172 and AFOSR-89-0410. It was conducted while the author was a Graduate Student at the Computer Sciences Department, University of Wisconsin, Madison, Wisconsin.     

A two-stage successive overrelaxation algorithm for solving the symmetric linear complementarity problem

We propose a two-stage successive overrelaxation method (TSOR) algorithm for solving the symmetric linear complementarity problem. After the first SOR preprocessing stage this algorithm concentrates on updating a certain prescribed subset of variables which is determined by exploiting the complementarity property. We demonstrate that this algorithm successfully solves problems with up to ten thousand variables.This material is based on research supported by National Science Foundation Grants DCR-8420963 and DCR-8521228 and Air Force Office of Scientific Research Grants AFSOR-86-0172 and AFSOR-86-0255 while the author was at the Computer Sciences Department at the University of Wisconsin-Madison, USA.     

An L-like model containing very large cardinals

We force and construct a model in which level by level equivalence between strong compactness and supercompactness holds, along with a strong form of diamond and a version of square consistent with supercompactness. This generalises a result due to the first author. There are no restrictions in our model on the structure of the class of supercompact cardinals. A. W. Apter’s research was partially supported by PSC-CUNY Grants and CUNY Collaborative Incentive Grants. J. Cummings’s research was partially supported by NSF Grant DMS-0400982.     

Visible surgery, four-dimensionals-cobordisms and related questions in geometric topology

The visible surgery theory of M. Weiss is used to complete the topological classification of four-dimensionals-cobordisms between three-dimensional spherical spaceforms. Applications to various questions concerning pseudo-isotopies of 3-manifolds, computations of the simple Wall groups of finite periodic groups, and inductive detection of self-homeomorphisms of manifolds are also discussed.The first named author was partially supported by a grant from the Louisiana Board of Regents and National Science Foundation Grants DMS 89-01583 and 91-01575, and the second named author was partially supported by National Science Foundation Grants DMS 86-02543, 89-02622 and 91-02711.     

Alternating direction splitting for block Angular parallel optimization

We develop and compare three decomposition algorithms derived from the method of alternating directions. They may be viewed as block Gauss-Seidel variants of augmented Lagrangian approaches that take advantage of block angular structure. From a parallel computation viewpoint, they are ideally suited to a data parallel environment. Numerical results for large-scale multicommodity flow problems are presented to demonstrate the effectiveness of these decomposition algorithmims on the Thinking Machines CM-5 parallel supercomputer relative to the widely-used serial optimization package MINOS 5.4.This material is based on research supported by the Air Force Office of Scientific Research, Grants AFORS-89-0410 and F49620-1-0036, and by NSF Grants CCR-89-07671, CDA-90-24618, and CCR-93-06807. The work of the second author was supported partially by Grant 95.00732.CT01 from the Italian National Research Council (CNR).     

Optimal dividends and bankruptcy procedures: Analysis of the Ornstein–Uhlenbeck process

This paper investigates the impact of bankruptcy procedures on optimal dividend barrier policies. We specifically focus on Chapter 11 of the US Bankruptcy Code, which allows a firm in default to continue its business for a certain period of time. Our model is based on the surplus of a firm that earns investment income at a constant rate of credit interest when it is in a creditworthy condition. The firm pays a debit interest rate that depends on the deficit level when it is in financial distress. Thus, the surplus follows an Ornstein–Uhlenbeck (OU) process with a negative surplus-dependent mean-reverting rate. Default and liquidation are modeled as distinguishable events by using an excursion time or occupation time framework. This paper demonstrates how the optimal dividend barrier can be obtained by deriving a closed-form solution for the dividend value function. It also characterizes the distributional property and expectation of bankruptcy time subject to the bankruptcy procedure. Our numerical examples show that under an optimal dividend barrier strategy, the bankruptcy procedure may not prolong the expected bankruptcy time in some situations.     

Optimal dividends and bankruptcy procedures: Analysis of the Ornstein-Uhlenbeck process

This paper investigates the impact of bankruptcy procedures on optimal dividend barrier policies. We specifically focus on Chapter 11 of the US Bankruptcy Code, which allows a firm in default to continue its business for a certain period of time. Our model is based on the surplus of a firm that earns investment income at a constant rate of credit interest when it is in a creditworthy condition. The firm pays a debit interest rate that depends on the deficit level when it is in financial distress. Thus, the surplus follows an Ornstein-Uhlenbeck (OU) process with a negative surplus-dependent mean-reverting rate. Default and liquidation are modeled as distinguishable events by using an excursion time or occupation time framework. This paper demonstrates how the optimal dividend barrier can be obtained by deriving a closed-form solution for the dividend value function. It also characterizes the distributional property and expectation of bankruptcy time subject to the bankruptcy procedure. Our numerical examples show that under an optimal dividend barrier strategy, the bankruptcy procedure may not prolong the expected bankruptcy time in some situations.     

Consistency of infinitesimal perturbation analysis estimators with rates

We study a class of infinitesimal perturbation analysis (IPA) algorithms for queueing systems with load-dependent service and/or arrival rates. Such IPA algorithms were originally motivated by applications to large queueing systems in conjunction with aggregation algorithms. We prove strong consistency of these estimators through a type of birth and death queue. This work was supported in part by the NSF under Grants Nos. ECS85-15449 and CDR-8803012, by ONR under Contracts Nos. N00014-89-J-0075 and N00014-90-K-1093, and by the US Army under Contract No. DAAL-03-83-K-0171. This paper was written while the author was with the Division of Applied Sciences at Harvard University.     

A globally convergent Newton method for convex SC1 minimization problems

This paper presents a globally convergent and locally superlinearly convergent method for solving a convex minimization problem whose objective function has a semismooth but nondifferentiable gradient. Applications to nonlinear minimax problems, stochastic programs with recourse, and their extensions are discussed.The research of the first author is based on work supported by the National Science Foundation under Grants DDM-9104078 and CCR-9213739. This research was carried out while he was visiting the University of New South Wales. The research of the second author is based on work supported by the Australian Research Council.     

A scheduling policy with maximal stability region for ring networks with spatial reuse

A slotted ring that allows simultaneous transmissions of messages by different users is considered. Such a ring network is commonly called ring withspatial reuse. It can achieve significantly higher throughput than standard token rings but it also raises the issue of fairness since some nodes may be prevented from accessing the ring for long time intervals. Policies that operate in cycles and guarantee that a certain number (quota) of packets will be transmitted by every node in every cycle have been considered before to deal with the fairness issue. In this paper we address the problem of designing a policy that results in a stable system whenever the end-to-end arrival rates are within the stability region of the ring with spatial reuse (the stability region of the ring is defined as the set of end-to-end arrival rates for which there is a policy that makes the ring stable). We provide such a policy, which does not require knowledge of end-to-end arrival rates. The policy is an adaptive version of the quota policies and can be implemented with the same distributed mechanism. We use the Lyapunov test function technique together with methods from the theory of regenerative processes to derive our main results.This research was primarily done while the author was visiting INRIA in Rocquencourt, France. The author wishes to thank INRIA (projects ALGO, MEVAL and REFLECS) for a generous support. Additional support was provided by NSF Grants NCR-9206315 and CCR-9201078 and INT-8912631, and by Grant AFOSR-90-0107, and in part by NATO Grant 0057/89.The research of this author was supported in part by NSF under Grants NCR-9211417 and NCR-9406415, and by the New York State Center for Advanced Technology in Telecommunications, Polytechnic University.     

Numerical Comparison of Augmented Lagrangian Algorithms for Nonconvex Problems

Augmented Lagrangian algorithms are very popular tools for solving nonlinear programming problems. At each outer iteration of these methods a simpler optimization problem is solved, for which efficient algorithms can be used, especially when the problems are large. The most famous Augmented Lagrangian algorithm for minimization with inequality constraints is known as Powell-Hestenes-Rockafellar (PHR) method. The main drawback of PHR is that the objective function of the subproblems is not twice continuously differentiable. This is the main motivation for the introduction of many alternative Augmented Lagrangian methods. Most of them have interesting interpretations as proximal point methods for solving the dual problem, when the original nonlinear programming problem is convex. In this paper a numerical comparison between many of these methods is performed using all the suitable problems of the CUTE collection.This author was supported by ProNEx MCT/CNPq/FAPERJ 171.164/2003, FAPESP (Grants 2001/04597-4 and 2002/00094-0 and 2003/09169-6) and CNPq (Grant 302266/2002-0).This author was partially supported by CNPq-Brasil and CDCHT-Venezuela.This author was supported by ProNEx MCT/CNPq/FAPERJ 171.164/2003, FAPESP (Grant 2001/04597-4) and CNPq.     

Market discipline of subordinated debt in banking: The case of costly bankruptcy

This paper extends the contingent-claim valuation framework of subordinated debt by explicitly incorporating bankruptcy cost in the model. The model is then used to investigate the role of subordinated debt in alleviating the moral hazard problem in banking. The incorporation of bankruptcy cost in the framework of the analysis provides new evidence about subordinated debt. The reaction of subordinated debt prices can completely remove risk-shifting incentives of equityholders only when bankruptcy costs are accounted for. The extent of subordinated debt’s discipline is shown to depend critically on the relative magnitude of subordinated debt, senior debt and bankruptcy costs.     

基于跳扩散过程的Omega模型

文章通过在Omega模型中加入布朗运动扰动项,提出了一种跳扩散Omega破产模型.在索赔额为指数分布的情形下,给出了破产率函数是常数时的破产概率函数表达式.文章进一步研究了破产概率和盈余过程的“负占有时”之间的关系,并给出了破产概率函数的第二种推导过程.最后通过两个数值试验,将我们的模型与Albreeher和Lautscham (2013)的Omega模型的破产概率进行了比较分析.     

Vector Ordinal Optimization

Ordinal optimization is a tool to reduce the computational burden in simulation-based optimization problems. So far, the major effort in this field focuses on single-objective optimization. In this paper, we extend this to multiobjective optimization and develop vector ordinal optimization, which is different from the one introduced in Ref. 1. Alignment probability and ordered performance curve (OPC) are redefined for multiobjective optimization. Our results lead to quantifiable subset selection sizes in the multiobjective case, which supplies guidance in solving practical problems, as demonstrated by the examples in this paper.This paper was supported in part by Army Contract DAAD19-01-1-0610, AFOSR Contract F49620-01-1-0288, and a contract with United Technology Research Center (UTRC). The first author received additional funding from NSF of China Grants 60074012 and 60274011, Ministry of Education (China), and a Tsinghua University (Beijing, China) Fundamental Research Funding Grant, and the NCET program of China.The authors are grateful to and benefited from two rounds of reviews from three anonymous referees.     

Parallel pivotal algorithm for solving the linear complementarity problem

We propose a parallel implementation of the classical Lemke's algorithm for solving the linear complementarity problem. The algorithm is designed for a loosely coupled network of computers which is characterized by relatively high communication costs. We provide an accurate prediction of speedup based on a simple operation count. The algorithm produces speedup nearp, wherep is the number of processors, when tested on large problems as demonstrated by computational results on the CRYSTAL token-ring multicomputer and the Sequent Balance 21000 multiprocessor.This material is based on research supported by National Science Foundation Grants DCR-84-20963 and DCR-850-21228 and by Air Force Office of Scientific Research Grants AFSOR-86-0172 and AFSOR-86-0255 while the author was at the University of Wisconsin, Madison, Wisconsin.     

A Continuous Method for Convex Programming Problems

In this paper, we present a continuous method for convex programming (CP) problems. Our approach converts first the convex problem into a monotone variational inequality (VI) problem. Then, a continuous method, which includes both a merit function and an ordinary differential equation (ODE), is introduced for the resulting variational inequality problem. The convergence of the ODE solution is proved for any starting point. There is no Lipschitz condition required in our proof. We show also that this limit point is an optimal solution for the original convex problem. Promising numerical results are presented.This research was supported in part by Grants FRG/01-02/I-39 and FRG/01-02/II-06 of Hong Kong Baptist University and Grant HKBU2059/02P from the Research Grant Council of Hong Kong.The author thanks Professor Bingsheng He for many helpful suggestions and discussions. The author is also grateful for the comments and suggestions of two anonymous referees. In particular, the author is indebted to one referee who drew his attention to References 15, 17, 18.