Discrete approximation of linear two–stage stochastic programming problem

This paper describes a possibility for approximate solution of stochastic programming problems with complete recourse. We replace the static form of linear problem in L^p-space by a sequence of discretized problems in finite-dimensional spaces. We present conditions that guarantee the convergence of optimal values of discretized problems to the optimal value of the initial problem.     

Adaptive use of iterative methods in predictor–corrector interior point methods for linear programming

In this work we devise efficient algorithms for finding the search directions for interior point methods applied to linear programming problems. There are two innovations. The first is the use of updating of preconditioners computed for previous barrier parameters. The second is an adaptive automated procedure for determining whether to use a direct or iterative solver, whether to reinitialize or update the preconditioner, and how many updates to apply. These decisions are based on predictions of the cost of using the different solvers to determine the next search direction, given costs in determining earlier directions. We summarize earlier results using a modified version of the OB1-R code of Lustig, Marsten, and Shanno, and we present results from a predictor–corrector code PCx modified to use adaptive iteration. If a direct method is appropriate for the problem, then our procedure chooses it, but when an iterative procedure is helpful, substantial gains in efficiency can be obtained.     

On Hyers–Ulam stability of generalized linear functional equation and its induced Hyers–Ulam programming problem

We propose a new approach called Hyers–Ulam programming to discriminate whether a generalized linear functional equation, with the form \({\sum_{i=1}^m L_if(\sum_{j=1}^n a_{ij}x_j) = 0}\) for functions from a normed space into a Banach space, has the Hyers–Ulam stability or not. Our main result is that if the induced Hyers–Ulam programming has a solution, then the corresponding functional equation possesses the Hyers–Ulam stability.     

Delta–Gamma hedging of mortality and interest rate risk

One of the major concerns of life insurers and pension funds is the increasing longevity of their beneficiaries. This paper studies the hedging problem of annuity cash flows when mortality and interest rates are stochastic. We first propose a Delta–Gamma hedging technique for mortality risk. The risk factor against which to hedge is the difference between the actual mortality intensity in the future and its “forecast” today, the forward intensity. We specialize the hedging technique first to the case in which mortality intensities are affine, then to Ornstein–Uhlenbeck and Feller processes, providing actuarial justifications for this selection. We show that, without imposing no arbitrage, we can get equivalent probability measures under which the HJM condition for no arbitrage is satisfied. Last, we extend our results to the presence of both interest rate and mortality risk. We provide a UK calibrated example of Delta–Gamma hedging of both mortality and interest rate risk.     

Heuristics and reduction methods for multiple constraints 0–1 linear programming problems

This work extends the efficient results relative to the 0–1 knapsack problem to the multiple inequality constraints 0–1 linear programming problems. The two crucial phases for the solving of this type of problems are presented: (i) Two linear expected time complexity greedy algorithms are proposed for the determination of a lower bound on the optimal value by using a cascade of surrogate relaxations of the original problem whose sizes are decreasing step by step. A comparative study with the best known heuristic methods is reported; it concerned the accuracy of the approximate solutions and the practical computational times. (ii) This greedy algorithm is inserted in an efficient reduction framework. Variables and constraints are eliminated by the conjunction of tests applied to Lagrangean and surrogate relaxations of the original problem. A lot of computational results are summarized by considering test problems of the literature.     

Maximum loss and maximum gain of spectrally negative Lévy processes

The joint distribution of the maximum loss and the maximum gain is obtained for a spectrally negative Lévy process until the passage time of a given level. Their marginal distributions up to an independent exponential time are also provided. The existing formulas for Brownian motion with drift are recovered using the particular scale functions.     

Perfect duality in semi–infinite and semidefinite programming

This paper develops new relationships between the recently constructed semidefinite programming perfect duality and the earlier perfect duality achieved for linear semi–infinite programming. Applying the linear semi–infinite perfect duality construction to semidefinite programming yields a larger feasible set than the one obtained by the newly constructed semidefinite programming regularized perfect dual. Received: November 10, 1997 / Accepted: March 5, 1999?Published online May 3, 2001     

Budget and long-range planning in public transportation — An applicative linear programming model

Economic and financial planning is an actual problem for Italian Transport Authorities, since in Italy there are scarce financial resources, to cover either the difference between costs and fares proceeds or investment needs. The proposed model is ‘just tailored’ on public transport, subjected to the Italian laws; it takes into account the particular activities (‘functions’) connected with public transport operating (e.g.: fare collection, service production, maintenance, purchases, inventories, administration). Each function is considered as a ‘module’ with its own constraints, and it is connected with the other ‘modules’, so that we can get a ‘representative’ model. Also the most significant parameters of the ‘public transport operating problem’ (e.g.: vehicle miles, passenger journeys, number of vehicles, number of employees, and so on) are connected one another. Since all relations and constraints, connecting the ‘problem variables’ can be represented as linear, the model structure is based on linear programming; this fact allows to pursue an optimum for one or more objective functions, each of them identifing an operational policy. So it is possible to connect simulation with optimization. A multi-period model can be used for long-range planning, pursuing a multi-year optimum; this is the most significant use. The Administrative Module contains Balance-Sheet, Cash-Flow, Profit and Loss Account, which are ‘constraints’ in the model and are expressed as Italian financial laws require; this structure is one of the main characteristics. Modules' dimensions and additional constraints (e.g. economic or financial ratios, fare level, turnover of inventories, and so on) can be ‘tailored’ on each particular case, driving the model towards more realistic solutions.     

First-order and second-order ε-directional derivatives of a marginal function in convex programming with linear inequality constraints

Hiriart-Urruty gave formulas of the first-order and second-order -directional derivatives of a marginal function for a convex programming problem with linear equality constraints, that is, the image of a function under linear mapping (Ref. 1). In this paper, we extend his results to a problem with linear inequality constraints. The formula of the first-order derivative is given with the help of a duality theorem. A lower estimate for the second-order -directional derivative is given.The author wishes to thank Professor N. Furukawa and Dr. H. Kawasaki for their helpful comments and encouragements. He is also indebted to one referee for pointing out the proof of Proposition 3.1.     

Coordinating advertising and pricing in a manufacturer–retailer channel

Cooperative advertising is a practice that a manufacturer pays retailers a portion of the local advertising cost in order to induce sales. Cooperative advertising plays a significant role in marketing programs of channel members. Nevertheless, most studies to date on cooperative advertising have assumed that the market demand is only influenced by advertising expenditures but not by retail price. This paper addresses channel coordination by seeking optimal cooperative advertising strategies and equilibrium pricing in a two-member distribution channel. We establish and compare two models: a non-cooperative, leader–follower game and a cooperative game. We develop propositions and insights from the comparison of these models. The cooperative model achieves better coordination by generating higher channel-wide profits than the non-cooperative model with these features: (a) the retailer price is lower to consumers; and (b) the advertising efforts are higher for all channel members. We identify the feasible solutions to a bargaining problem where the channel members can determine how to divide the extra profits.     

Investments in EOP-technologies and emissions trading – Results from a linear programming approach and sensitivity analysis

This paper offers a general approach to valuating investments in end-of-pipe-technologies (EOP-technologies) with special regard to an emissions trading scheme. Since investments of this kind affect production, it is necessary to derive the required payments for a financial valuation and the constraints from production theory and production planning. On this basis, it is possible to develop a valuation model. This model considers joint production, activity-level-dependent and -independent payments and specifically includes the indivisibility of the investment. Applying duality theory enables us to examine the determinants of the price ceiling for such an investment. Sensitivity analysis shows that tradable permits have several effects on an investment and do not always encourage environmentally beneficial investments – in particular cases they may even be counterproductive.     

Mean and variance optimization of non–linear systems and worst–case analysis

In this paper, we consider expected value, variance and worst–case optimization of nonlinear models. We present algorithms for computing optimal expected value, and variance policies, based on iterative Taylor expansions. We establish convergence and consider the relative merits of policies based on expected value optimization and worst–case robustness. The latter is a minimax strategy and ensures optimal cover in view of the worst–case scenario(s) while the former is optimal expected performance in a stochastic setting. Both approaches are used with a small macroeconomic model to illustrate relative performance, robustness and trade-offs between the alternative policies.     

Solving fuzzy multi-objective linear programming problems using deviation degree measures and weighted max–min method

This paper proposes a method for solving fuzzy multi-objective linear programming (FMOLP) problems where all the coefficients are triangular fuzzy numbers and all the constraints are fuzzy equality or inequality. Using the deviation degree measures and weighted max–min method, the FMOLP problem is transformed into crisp linear programming (CLP) problem. If decision makers fix the values of deviation degrees of two side fuzzy numbers in each constraint, then the δ-pareto-optimal solution of the FMOLP problems can be obtained by solving the CLP problem. The bigger the values of the deviation degrees are, the better the objectives function values will be. So we also propose an algorithm to find a balance-pareto-optimal solution between two goals in conflict: to improve the objectives function values and to decrease the values of the deviation degrees. Finally, to illustrate our method, we solve a numerical example.     

Integral representation and computational properties of the incomplete Fox–Wright function

The Ramanujan Journal - The main focus of this paper is to investigate some new properties of the incomplete Fox–Wright function. We derive an integral representation of the incomplete...     

Solving non–linear optimal stopping problems by the method of time–change

Some non–linear optimal stopping problems can be solved explicitly by using a common method which is based on time–change.We describe this method and illustrate its use by considering several examples dealing with Brownian motion.In each of these examples we derive explicit formulas for the value function and display the optimal stopping time. The main emphasis of the paper is on the method of proof and its unifying scope     

Asymptotic behavior of integer programming and the stability of the Castelnuovo–Mumford regularity

Mathematical Programming - The paper provides a connection between Commutative Algebra and Integer Programming and contains two parts. The first one is devoted to the asymptotic behavior of integer...