The usefulness of Bayesian optimal designs for discrete choice experiments

Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated choice experiments or conjoint choice experiments, has gained much attention, stimulating the development of Bayesian choice design algorithms. Characteristic for the Bayesian design strategy is that it incorporates the available information about people's preferences for various product attributes in the choice design. This is in contrast with the linear design methodology, which is also used in discrete choice design and which depends for any claims of optimality on the unrealistic assumption that people have no preference for any of the attribute levels. Although linear design principles have often been used to construct discrete choice experiments, we show using an extensive case study that the resulting utility‐neutral optimal designs are not competitive with Bayesian optimal designs for estimation purposes. Copyright © 2011 John Wiley & Sons, Ltd.     

Dynamic portfolio selection with market impact costs

This paper concerns optimal dynamic portfolio choice with quadratic utility when there are market impact costs. The optimal policy is difficult to characterize, so we look instead for sub-optimal policies. Our proposed suboptimal policy solves a tractable dynamic portfolio choice problem where the cost of trading is captured in the objective instead of the price dynamics. A multiple time scale asymptotic expansion shows that our proposed policy has sensible structural properties, while numerical experiments show promising performance and robustness properties.     

提取医用黄腐酸的优化方案

本文介绍如何用正交试验法选择提取医用黄腐酸的最优方案 ,使提取出的医用黄腐酸达到最佳提取率 ,并符合国家药典标准     

An optimal 25-point finite difference scheme for the Helmholtz equation with PML

In this paper, we present an optimal 25-point finite difference scheme for solving the Helmholtz equation with perfectly matched layer (PML) in two dimensional domain. Based on minimizing the numerical dispersion, we propose the refined choice strategy for choosing optimal parameters of the 25-point finite difference scheme. Numerical experiments are given to illustrate the improvement of the accuracy and the reduction of the numerical dispersion.     

An optimal 25-point finite difference scheme for the Helmholtz equation with PML

In this paper, we present an optimal 25-point finite difference scheme for solving the Helmholtz equation with perfectly matched layer (PML) in two dimensional domain. Based on minimizing the numerical dispersion, we propose the refined choice strategy for choosing optimal parameters of the 25-point finite difference scheme. Numerical experiments are given to illustrate the improvement of the accuracy and the reduction of the numerical dispersion.     

A posteriori parameter choice strategy for fast multiscale methods solving ill-posed integral equations

We apply fast multiscale methods for solving ill-posed integral equations via the Tikhonov regularization. A modified a posteriori parameter choice strategy is presented, which leads to optimal convergence rates. Numerical experiments are given to illustrate the efficiency of the method.     

基于减排框架的最优技术政策选择研究

基于减排框架,本文分别引入研发补贴政策、研发卡特尔(简称“R&D卡特尔”)、竞争研发联盟(简称“RJV竞争”)及卡特尔研发联盟(简称“RJV卡特尔”)等四种技术政策,构建了一个多阶段博弈模型,研究单一技术政策、技术组合政策情形下双寡头企业减排研发绩效、利润及社会福利水平,并进一步利用数值模拟方法探讨政府和企业最优的技术政策选择,政府和企业最优技术政策选择结果的差异及其影响因素。研究结论表明:首先,从政府角度来看,组合政策的效果总是相对优于单一技术政策,并且政府最优的技术政策选择为RJV卡特尔与补贴组合政策。其次,无论溢出率取何值,政府和企业最优的技术政策选择都是一致的。最后,排污税取值影响政府和企业最优技术选择的一致性,当排污税较小时,企业最优的技术政策选择为RJV竞争与补贴组合政策(或RJV竞争政策);当排污税较大时,企业最优的技术选择为单一的技术政策。为了使政府和企业的最优技术政策选择一致,政府应选择一个合适的排污税。     

Maximum Principle for Risk-Sensitive Stochastic Optimal Control Problem and Applications to Finance

This article is concerned with a risk-sensitive stochastic optimal control problem motivated by a kind of optimal portfolio choice problem in the financial market. The maximum principle for this kind of problem is obtained, which is similar in form to its risk-neutral counterpart. But the adjoint equations and maximum condition heavily depend on the risk-sensitive parameter. This result is used to solve a kind of optimal portfolio choice problem and the optimal portfolio choice strategy is obtained. Computational results and figures explicitly illustrate the optimal solution and the sensitivity to the volatility rate parameter.     

Network revenue management with inventory-sensitive bid prices and customer choice

We develop an approximate dynamic programming approach to network revenue management models with customer choice that approximates the value function of the Markov decision process with a non-linear function which is separable across resource inventory levels. This approximation can exhibit significantly improved accuracy compared to currently available methods. It further allows for arbitrary aggregation of inventory units and thereby reduction of computational workload, yields upper bounds on the optimal expected revenue that are provably at least as tight as those obtained from previous approaches. Computational experiments for the multinomial logit choice model with distinct consideration sets show that policies derived from our approach can outperform some recently proposed alternatives, and we demonstrate how aggregation can be used to balance solution quality and runtime.     

An efficient step size control for continuation methods

In solving a nonlinear equation by the use of a continuation method one of the crucial problems is the choice of the step sizes. We present a model for the total computational cost of a standard numerical continuation process and solve the problem of optimal step size control for this model. Using the theoretical results as a basis, we develop an adaptive step size algorithm for Newton's method. This procedure is computationally inexpensive and it gives quite satisfactory results compared to some other numerical experiments found in the literature.     

Generalized shift-splitting iteration method for a class of two-by-two linear systems

In this paper, we propose a generalized-shift splitting (GSS) iteration method for solving a broad class of two-by-two linear systems. The convergence theory of the GSS iteration method is established and the spectral properties of the corresponding preconditioned matrix of some special choices for the parameter matrices are analyzed. In addition, the optimal choice of the iterative parameter is discussed. Numerical experiments are used to verify the validity of the theoretical results and the effectiveness of the new method.     

AN ADAPTIVE VERSION OF GLIMM＇S SCHEME

This article describes a local error estimator for Glimm＇s scheme for hyperbolic systems of conservation laws and uses it to replace the usual random choice in Glimm＇s scheme by an optimal choice. As a by-product of the local error estimator, the procedure provides a global error estimator that is shown numerically to be a very accurate estimate of the error in L1 （R） for all times. Although there is partial mathematical evidence for the error estimator proposed, at this stage the error estimator must be considered ad- hoc. Nonetheless, the error estimator is simple to compute, relatively inexpensive, without adjustable parameters and at least as accurate as other existing error estimators. Numerical experiments in 1-D for Burgers＇ equation and for Euler＇s system are performed to measure the asymptotic accuracy of the resulting scheme and of the error estimator.     

AN ADAPTIVE VERSION OF GLIMM'S SCHEME

This article describes a local error estimator for Glimm's scheme for hyperbolic systems of conservation laws and uses it to replace the usual random choice in Glimm's scheme by an optimal choice.As a by-product of the local error estimator,the procedure provides a global error estimator that is shown numerically to be a very accurate estimate of the error in L1(R) for all times.Although there is partial mathematical evidence for the error estimator proposed,at this stage the error estimator must be considered adhoc.Nonetheless,the error estimator is simple to compute,relatively inexpensive,without adjustable parameters and at least as accurate as other existing error estimators.Numerical experiments in 1-D for Burgers' equation and for Euler's system are performed to measure the asymptotic accuracy of the resulting scheme and of the error estimator.     

一个组面试问题的最佳顺序安排

采用边界阶段方法讨论最优组面试问题的最佳划分选择策略,在允许调整面试顺序的情况下,得到最优组面试问题中如何重新安排面试顺序能够以最大概率录用到最优应聘者的排序策略.     

Multi-parameter regularization and its numerical realization

In this paper we propose and analyse a choice of parameters in the multi-parameter regularization of Tikhonov type. A modified discrepancy principle is presented within the multi-parameter regularization framework. An order optimal error bound is obtained under the standard smoothness assumptions. We also propose a numerical realization of the multi-parameter discrepancy principle based on the model function approximation. Numerical experiments on a series of test problems support theoretical results. Finally we show how the proposed approach can be successfully implemented in Laplacian Regularized Least Squares for learning from labeled and unlabeled examples.     

Implicit depot assignments and rotations in vehicle routing heuristics

Vehicle routing variants with multiple depots and mixed fleet present intricate combinatorial aspects related to sequencing choices, vehicle type choices, depot choices, and depots positioning. This paper introduces a dynamic programming methodology for efficiently evaluating compound neighborhoods combining sequence-based moves with an optimal choice of vehicle and depot, and an optimal determination of the first customer to be visited in the route, called rotation. The assignment choices, making the richness of the problem, are thus no more addressed in the solution structure, but implicitly determined during each move evaluation. Two meta-heuristics relying on these concepts, an iterated local search and a hybrid genetic algorithm, are presented. Extensive computational experiments demonstrate the remarkable performance of these methods on classic benchmark instances for multi-depot vehicle routing problems with and without fleet mix, as well as the notable contribution of the implicit depot choice and positioning methods to the search performance. New state-of-the-art results are obtained for multi-depot vehicle routing problems (MDVRP), and multi-depot vehicle fleet mix problems (MDVFMP) with unconstrained fleet size. The proposed concepts are fairly general, and widely applicable to many other vehicle routing variants.     

Integrated fleet mix and routing decision for hazmat transportation: A developing country perspective

In developing countries, truck purchase cost is the dominant criteria for fleet acquisition-related decisions. However, we contend that other cost factors such as loss due to the number of en route truck stoppages based on a truck type and recovery cost associated with a route choice decision, should also be considered for deciding the fleet mix and minimizing the overall costs for long-haul shipments. The resulting non-linear model, with integer variables for the number and type of trucks, and the route choices, is solved via genetic algorithm. Using real data from a bulk liquid hazmat transporter, the trade-offs between the cost of travel, loss due to number of truck stoppages, and the long-term recovery cost associated with the risk of exposure due to a hazmat carrier accident are discussed. The numerical experiments show that when factors related to public safety and truck stoppages are taken into account for transportation, the lowest total cost and optimal route choice do not align with the cheapest truck type option; rather, the optimal solution corresponds to another truck type and route with total costs significantly less than the total costs associated with the cheapest truck type. Our model challenges the current truck purchasing strategy adopted in developing countries using the cheapest truck criteria.     

A generalization of Arcangeli's method for ill-posed problems leading to optimal rates

Schock (1984) considered a general a posteriori parameter choice strategy for the regularization of ill-posed problems which provide nearly the optimal rate of convergence. We improve the result of Schock and give a class of parameter choice strategies leading to optimal rates As a particular case we prove that the Arcangeli's method do give optimal rate of convergence.     

基于战略顾客行为的最优定价与容量选择模型

研究了当市场中同时存在战略顾客和短视顾客时零售商的最优定价与容量选择问题。零售商在正常销售阶段和出清销售阶段制定不同的销售价格,同时通过容量选择影响战略顾客的购买行为,而战略顾客则根据零售商的定价和容量选择确定最优购买时机。分别分析了零售商在无限容量时的定价决策、固定价格时的容量选择、固定容量时的定价决策以及有限容量下的定价与容量选择四种情形。研究结果表明,零售商在无容量限制时的最优定价决策是制定两阶段定价策略,在固定价格时的最优容量选择依赖于模型的参数,而当零售商的容量固定时,部分满足出清销售阶段的顾客需求始终优于完全满足出清销售阶段的顾客需求。     

跳扩散最优控制的随机最大值原理及在金融中的应用

讨论了由金融市场中投资组合和消费选择问题引出的一类最优控制问题,投资者的期望效用是常数相对风险厌恶(CRRA)情形.在跳扩散框架下,利用古典变分法得到了一个局部随机最大值原理.结果应用到最优投资组合和消费选择策略问题,得到了状态反馈形式的显式最优解.