An exact method for constrained maximization of the conditional value-at-risk of a class of stochastic submodular functions

We consider a class of risk-averse submodular maximization problems (RASM) where the objective is the conditional value-at-risk (CVaR) of a random nondecreasing submodular function at a given risk level. We propose valid inequalities and an exact general method for solving RASM under the assumption that we have an efficient oracle that computes the CVaR of the random function. We demonstrate the proposed method on a stochastic set covering problem that admits an efficient CVaR oracle for the random coverage function.     

Robust optimal reinsurance–investment strategy with price jumps and correlated claims

This paper considers the robust optimal reinsurance–investment strategy selection problem with price jumps and correlated claims for an ambiguity-averse insurer (AAI). The correlated claims mean that future claims are correlated with historical claims, which is measured by an extrapolative bias. In our model, the AAI transfers part of the risk due to insurance claims via reinsurance and invests the surplus in a financial market consisting of a risk-free asset and a risky asset whose price is described by a jump–diffusion model. Under the criterion of maximizing the expected utility of terminal wealth, we obtain closed-form solutions for the robust optimal reinsurance–investment strategy and the corresponding value function by using the stochastic dynamic programming approach. In order to examine the influence of investment risk on the insurer’s investment behavior, we further study the time-consistent reinsurance–investment strategy under the mean–variance framework and also obtain the explicit solution. Furthermore, we examine the relationship among the optimal reinsurance–investment strategies of the AAI under three typical cases. A series of numerical experiments are carried out to illustrate how the robust optimal reinsurance–investment strategy varies with model parameters, and result analyses reveal some interesting phenomena and provide useful guidances for reinsurance and investment in reality.     

Efficient solutions for weight-balanced partitioning problems

We prove polynomial-time solvability of a large class of clustering problems where a weighted set of items has to be partitioned into clusters with respect to some balancing constraints. The data points are weighted with respect to different features and the clusters adhere to given lower and upper bounds on the total weight of their points with respect to each of these features. Further the weight-contribution of a vector to a cluster can depend on the cluster it is assigned to. Our interest in these types of clustering problems is motivated by an application in land consolidation where the ability to perform this kind of balancing is crucial.Our framework maximizes an objective function that is convex in the summed-up utility of the items in each cluster. Despite hardness of convex maximization and many related problems, for fixed dimension and number of clusters, we are able to show that our clustering model is solvable in time polynomial in the number of items if the weight-balancing restrictions are defined using vectors from a fixed, finite domain. We conclude our discussion with a new, efficient model and algorithm for land consolidation.     

一维Allen-Cahn方程紧差分格式的离散最大化原则和能量稳定性研究

本文主要研究相场模拟中的Allen-Cahn模型,考虑一维Allen-Cahn方程紧差分方法的数值逼近.建立具有O(∫τ2+h4)精度的全离散紧差分格式,证明在合理的步长比和时间步长的约束下,其数值解满足离散最大化原则,在此基础上,研究了全离散格式的能量稳定性.最后给出数值算例.     

Estimates of the horizon in mathematical models of marketing

The economical concept of an horizon is redefined in stricter mathematical terms. This implies an approximation procedure, formulated in a semidefinite function space, which may be applied to many time-dependent social processes, resembling a real expontential-like growth ‘on the horizon’.     

Convergence of Utility Indifference Prices to the Superreplication Price

A discrete-time financial market model is considered with a sequence of investors whose preferences are described by concave strictly increasing functions defined on the positive axis. Under suitable conditions, we show that the utility indifference prices of a bounded contingent claim converge to its superreplication price when the investors’ absolute risk-aversion tends to infinity.     

A control approach to robust utility maximization with logarithmic utility and time-consistent penalties

We propose a stochastic control approach to the dynamic maximization of robust utility functionals that are defined in terms of logarithmic utility and a dynamically consistent convex risk measure. The underlying market is modeled by a diffusion process whose coefficients are driven by an external stochastic factor process. In particular, the market model is incomplete. Our main results give conditions on the minimal penalty function of the robust utility functional under which the value function of our problem can be identified with the unique classical solution of a quasilinear PDE within a class of functions satisfying certain growth conditions. The fact that we obtain classical solutions rather than viscosity solutions facilitates the use of numerical algorithms, whose applicability is demonstrated in examples.     

Proper equality constraints and maximization of index vectors

In solving many practical problems, we have to deal with conflictive multiple objectives (in performance, cost, gain, or payoff, etc). Can all such objectives be achieved simultaneously? The general answer is negative. That is, most multiple-objective problems do not have supreme solutions that can satisfy all of the objectives. Many broader definitions of optimality like Pareto optimum, efficient point, noninferior point, etc, have been introduced in various contexts, so that most multiple-objective problems can have optimal solutions. But such optimal solutions do not in general yield unique vectors of optimal indexes of the conflictive multiple objectives. In most cases, we have to make appropriate tradeoffs, compromises, or choices, among those optimal solutions. To obtain the set of all such optimal solutions (in particular, the set of all optimal index vectors), say for a comprehensive study on appropriate tradeoffs, compromises, or choices, a usual practice is to optimize linear combinations of the multiple-objective functions for various weights. The success of such approach relies heavily on a certain directional convexity condition; in other words, if such convexity is absent, this method will fail to obtain essential subsets. The method of proper equality constraints (PEC), however, relies on no convexity condition at all, and through it we can obtain the entire set. In this paper, we attempt to lay the foundation for the method of PEC. We are mainly concerned with obtaining the set of all maximal index vectors, for most of the broader-sense optimal solutions are actually expressed in terms of maximal index vectors (Ref. 1). First, we introduce the notion of quasisupremal vector as a substantially equivalent substitute for, but a rather practical and useful extension of, the notion of maximal vector. Then, we propose and develop the method of PEC for computing the set of all quasisupremal (or maximal) index vectors. An illustrative example in the allocation of funds is given. One of the important conclusions is that optimizing the index of one objective with the indexes of all other objectives equated to some arbitrary constants may still result in inferior solutions. The sensitivity to variations in these constants are examined, and various tests for quasisupremality (maximality, or optimality) are derived in this paper.