Minimizing lifetime poverty with a penalty for bankruptcy

We provide investment advice for an individual who wishes to minimize her lifetime poverty, with a penalty for bankruptcy or ruin. We measure poverty via a non-negative, non-increasing function of (running) wealth. Thus, the lower wealth falls and the longer wealth stays low, the greater the penalty. This paper generalizes the problems of minimizing the probability of lifetime ruin and minimizing expected lifetime occupation, with the poverty function serving as a bridge between the two. To illustrate our model, we compute the optimal investment strategies for a specific poverty function and two consumption functions, and we prove some interesting properties of those investment strategies.     

Valuing inflation-linked death benefits under a stochastic volatility framework

In this paper we construct a framework to price the inflation-linked derivatives with the stochastic inflation rate, the stochastic interest rate, and stochastic risky assets with stochastic volatility. Because of the popularity of the guaranteed minimum death benefit (GMDB) in insurance market, we mainly study two types of GMDBs: the inflation guarantee and the combination guarantee. We consider the guaranteed minimum death benefit as an European option with a random maturity date, the closed-form pricing formulas for the GMDBs are derived by Fourier-based method. Moreover, we give an elaborate sensitivity analysis to explain economical behaviors of our models. The numerical results show that the death benefit of inflation guarantee is slightly overpriced in constant volatility of stock situation.     

On the global asymptotic stability and oscillation of solutions in a stochastic business cycle model

We discuss a second order nonlinear stochastic difference equation which is constructed of a business cycle model with organized labor considered. A global asymptotic mean square stability criterion is obtained by Lyapunov function method. We also prove a theorem on the almost sure oscillation of the solutions for the difference equation with state-independent stochastic perturbations.     

Rational solutions of the graphsack problem

A graphsack problem is a certain binary linear optimization problem with applications in optimal network design. From there a rational graphsack problem is derived by allowing the variables to vary continuously between 0 and 1. In this paper we deal with rational graphsack problems. First we develop the concept of compressed solutions and the concept of augmenting cuts. Making use of these concepts a very simple optimality criterion is derived. Finally an efficient algorithm solving rational graphsack problems is given which is polynomially bounded in time and which is closely related to the simplex algorithm.     

A note on limiting infisup theorems

We show that the limiting infisup theorem of Blair, Duffin and Jeroslow (1982) is a consequence of the classical bifunctional duality. By doing so we generalize their results and prove another limiting infisup theorem for convex quasi-concave functions.     

Generalized semi-infinite programming: numerical aspects

Generalized semi-infinite optimization problems (GSIP) are considered. It is investigated how the numerical methods for standard semi-infinite programming (SIP) can be extended to GSIP. Newton methods can be extended immediately. For discretization methods the situation is more complicated. These difficulties are discussed and convergence results for a discretization- and an exchange method are derived under fairly general assumptions on GSIP. The question is answered under which conditions GSIP represents a convex problem.     

Relaxation techniques applied to some loading problems

In this paper a two-stage loading problem, dealing with allocation of jobs to machines, is studied. The outer problem is to choose a subset among a number of available machines such that a feasible assigment exists and the total cost price is minimized. The inner problem, is then to find the optimal allocation, given the subset of machines and some assigment criterion at this lower level. It is shown that the choice of problem formulation can be crucial for the strength of the continuous relaxation. Computational results are also presented     

Dimension and finite reduction in linear semi-infinite programming

In the first part, different characterizations for the dimension of the feasible set in linear semi-infinite programming are provided. They involve the corresponding dimensions of some parameter sets, as the consequent inequalities cone and its lineality subspace. The remaining sections of the paper deal with Farkas–Minkowski systems. The third section is devoted to establish some results concerning the optimal set and its dimension, exploiting its strong relation with a particular parameter cone

associated with the corresponding unstable constraints. The last section approaches the finite reducibility problem. We have intended to characterize those finite subproblems with the same optimal value as the original problem, by means of a simplc dual analysis, based on the main results derived before.     

The technical efficiency of vacuum-pan sugar industry of India: An application of a stochastic frontier production function using panel data

A stochastic production frontier method is used to examine technical efficiency among Indian vacuum-pan sugar factories over a five-year period. Most factories are close to Indian best practice in terms of technical efficiency. Smaller firms and firms with access to sweeter cane are likely to be more efficient than other firms, while publicly owned firms are less efficient. There are transitory positive effects of a long crushing season on technical efficiency.     

Effective Information for Offline Stochastic Feedback and Optimal Control of Dynamic Systems

The impact of uncertain future events on decision making in a stochastic environment is modeled in this paper. Such modeling is presented for both feedback and optimal control problems. This research overcomes the difficulties of forecasting that arise when considering future information. In this paper, we seek to find the minimum amount of information (effective information) necessary to evaluating system performance offline or to optimally control a system. The existence of effective information is proved and a methodology for determining it is developed. It is also shown that ignoring information beyond the planning horizon leads to significant performance loss and may even lead to violating the constraints of a control problem.