1,687 research outputs found
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 GāL 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 deļ¬ne 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 efļ¬cient 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 meanāvariance portfolio optimization based on Monte-Carlo simulation
We propose a simulation-based approach for solving the constrained dynamic meanā
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):831ā873, 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
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