A new approximation algorithm for the multilevel facility location problem

In this paper we propose a new integer programming formulation for the multi-level facility location problem and a novel 3-approximation algorithm based on LP rounding. The linear program we are using has a polynomial number of variables and constraints, being thus more efficient than the one commonly used in the approximation algorithms for this type of problems

Analytical Solution for facilitated transport across a membrane

An analytical expression for the facilitation factor of component A across a liquid membrane is derived in case of an instantaneous reaction A(g)+B(l)AB(l) inside the liquid membrane. The present expression has been derived based on the analytical results of Olander (A.I.Ch.E. J. 6(2) (1960) 233) obtained for the enhancement factor for G–L systems with bulk. The analytical expression for the facilitation factor allows for arbitrary diffusivities of all species involved and does not contain any simplification or approximations. The facilitation factor starts from the value of unity, goes through a maximum and then reduces back to unity as the equilibrium constant is increased. The maximum facilitation factor occurs at higher values of the equilibrium constant as the ratio of the permeate-complex over carrier diffusivity is reduced whereas the maximum facilitation factor occurs at the same value of the equilibrium constant for all values of DA/DB (ratio of the permeate over carrier diffusivity). A similar behavior is seen for the flux of A as a function of the equilibrium constant. The facilitation factor remains constant with changes in the film thickness whereas the flux of A reduces with an increase in the thickness of the film. A linear increase of the facilitation factor and flux of A are seen with increasing initial carrier concentration

Reusability of coordination programs

Isolating computation and communication concerns into separate pure computation and pure coordination modules enhances modularity, understandability, and reusability of parallel and/or distributed software. This can be achieved by moving communication primitives (such as SendMessage and ReceiveMessage), which are now commonly scattered in application codes, into separate modules written in a language dedicated to the coordination of processes and the flow of information among them. MANIFOLD is a pure coordination language that encourages the separation of communication and computation concerns, We use real, concrete, running MANIFOLD programs to demonstrate the concept of pure coordination modules and the advantage of their reuse in applications of different nature

Coordination of distributed/parallel multiple-grid domain decomposition

A workable approach for the solution of many (numerical and non-numerical) problems is domain decomposition. If a problem can be divided into a number of sub-problems that can be solved in a distributed/parallel fashion, the overall performance can significantly improve. In this paper, we discuss one of our experiments using the new coordination language MANIFOLD to solve an instance of the classical optimization problem by domain decomposition. We demonstrate the applicability of MANIFOLD in expressing the solutions to domain decomposition problems in a generic way and its utility in producing executable code that can carry out such solutions in both distributed and parallel environments. The multiple-grid domain decomposition method used in this paper is based on adaptive partitioning of the domain and results in highly irregular grids as shown in the examples. The implementation of the distributed/parallel approach presented in this paper looks very promising and its coordinator modules are generally applicable

On pre-commitment aspects of a time-consistent strategy for a mean-variance investor

In this paper, a link between a time-consistent and a pre-commitment investment strategy is established. We define an implied investment target, which is implicitly con- tained in a time-consistent strategy at a given time step and wealth level. By imposing the implied investment target at the initial time step on a time-consistent strategy, we form a hybrid strategy which may generate better mean-variance efficient frontiers than the time-consistent strategy. We extend the numerical algorithm proposed in Cong and Oosterlee (2016b) to solve constrained time-consistent mean-variance optimization pro- blems. Since the time-consistent and the pre-commitment strategies generate different terminal wealth distributions, time-consistency is not always inferior to pre-commitment

Pricing Bermudan options under Merton jump-diffusion asset dynamics

In this paper, a recently developed regression-based option pricing method, the Stochastic Grid Bundling Method (SGBM), is considered for pricing multidimensional Bermudan options.We compare SGBM with a traditional regression-based pricing approach and present detailed insight in the application of SGBM, including how to configure it and how to reduce the uncertainty of its estimates by control variates. We consider the Merton jump-diffusion model, which performs better than the geometric Brownian motion in modelling the heavy-tailed features of asset price distributions. Our numerical tests show that SGBM with appropriate set-up works highly satisfactorily for pricing multidimensional options under jump-diffusion asset dynamics

Parsing User Queries using Context Free Grammars

In legal information retrieval, query cooking can significantly improve recall and precision. Context free grammars can be used to effectively parse user queries, even if the number of items torecognize is high and recognition patterns are complicated

Multi-period mean–variance portfolio optimization based on Monte-Carlo simulation

We propose a simulation-based approach for solving the constrained dynamic mean– variance portfolio managemen tproblem. For this dynamic optimization problem, we first consider a sub-optimal strategy, called the multi-stage strategy, which can be utilized in a forward fashion. Then, based on this fast yet sub-optimal strategy, we propose a backward recursive programming approach to improve it. We design the backward recursion algorithm such that the result is guaranteed to converge to a solution, which is at leas tas good as the one generated by the multi-stage strategy. In our numerical tests, highly satisfactory asset allocations are obtained for dynamic portfolio management problems with realistic constraints on the control variable

Accurate and Robust Numerical Methods for the Dynamic Portfolio Management Problem

This paper enhances a well-known dynamic portfolio management algorithm, the BGSS algorithm, proposed by Brandt et al. (Review of Financial Studies, 18(3):831–873, 2005). We equip this algorithm with the components from a recently developed method, the Stochastic Grid Bundling Method (SGBM), for calculating conditional expectations. When solving the first-order conditions for a portfolio optimum, we implement a Taylor series expansion based on a nonlinear decomposition to approximate the utility functions. In the numerical tests, we show that our algorithm is accurate and robust in approximating the optimal investment strategies,which are generated b