36670641
A nonlinear Lagrangian dual for integer programming
Nonlinear Lagrangian theory offers a success guarantee for the dual search via construction of a nonlinear support of the perturbation function at the optimal point. In this paper, a new nonlinear dual formulation of an exponential form is proposed for bounded integer programming. This new formulation possesses an asymptotic strong duality property and guarantees a success in identifying a primal optimum solution. No actual dual search is needed in the solution process when the parameter of the nonlinear Lagrangian formulation is set to be large enough.
46474884
Explicit Efficient Frontier of a Continuous-Time Mean Variance Portfolio Selection Problem
In this work we consider a continuous-time mean-variance portfolio selection problem that is formulated as a bi-criteria optimization problem. The objective is to maximize the expected return and minimize the variance of the terminal wealth. By putting weights on the two criteria one obtains a single objective stochastic control problem which is however not in the standard form. We show that this non-standard problem can be “embedded” into a class of auxiliary stochastic linear-quadratic (LQ) problems. By solving the latter based on the recent development on stochastic LQ problems with indefinite control weighting matrices, we derive the efficient frontier in a closed form for the original mean-variance problem.
207726898
Quick response policy with Bayesian information updates
Abstract In this paper we investigate the quick response (QR) policy with different Bayesian models. Under QR policy, a retailer can collect market information from the sales of a pre-seasonal product whose demand is closely related to a seasonal product’s demand. This information is then used to update the distribution for the seasonal product’s demand by a Bayesian approach. We study two information update models: one with the revision of an unknown mean, and the other with the revision of both an unknown mean and an unknown variance. The impacts of the information updates under both models are compared and discussed. We also identify the features of the pre-seasonal product which can bring more significant profit improvement. We conclude that an effective QR policy depends on a precise information update model as well as a selection of an appropriate pre-seasonal product as the observation target.
1427576
On a New Homotopy Continuation Trajectory for Nonlinear Complementarity Problems
Most known continuation methods for P 0 complementarity problems require some restrictive assumptions, such as the strictly feasible condition and the properness condition, to guarantee the existence and the boundedness of certain homotopy continuation trajectory. To relax such restrictions, we propose a new homotopy formulation for the complementarity problem based on which a new homotopy continuation trajectory is generated. For P 0 complementarity problems, the most promising feature of this trajectory is the assurance of the existence and the boundedness of the trajectory under a condition that is strictly weaker than the standard ones used widely in the literature of continuation methods. Particularly, the often-assumed strictly feasible condition is not required here. When applied to P * complementarity problems, the boundedness of the proposed trajectory turns out to be equivalent to the solvability of the problem, and the entire trajectory converges to the (unique) least element solution provided that it exists. Moreover, for monotone complementarity problems, the whole trajectory always converges to a least 2-norm solution provided that the solution set of the problem is nonempty. The results presented in this paper can serve as a theoretical basis for constructing a new path-following algorithm for solving complementarity problems, even for the situations where the solution set is unbounded.
17075751
A collaboration specification language
COCA (Collaborative Objects Coordination Architecture) was proposed as a novel means to model and support collaborations over the Internet. Our approach separates coordination policies from user interfaces and the policies are specified in a logic-based language. Over the past year, both the collaboration model and the specification language have been substantially refined and evaluated through our experience in building real-life collaboration systems. This paper presents the design of the specification language and illustrates the main ideas with a few simple design examples. Semantics, implementation, runtime support, and applications are also covered but not as the focus of this paper.
7412637
EXEC: An Evolvable and eXtensible Environment for Collaboration
This paper presents an evolvable and extensible environment for collaboration (EXEC), which aims to address the long-identified flexibility and reusability issues in collaborative systems. Due to the clean separation between data and control, both user interface components (data) and coordination service components (control) can be reused for developing collaborative applications and be flexibly bound at run time to address the evolutionary needs of collaboration tasks. Additionally, new collaboration tools can be easily prototyped to extend the functionality of the environment
17885452
Commutativity-based concurrency control in groupware
Commutativity of operations is often exploited in concurrent systems to attain high levels of concurrency. A commutativity-based concurrency control method, called operational transformation (OT), has been actively researched in groupware over the past 15 years. However, much progress can still be made on more practicable approaches to developing and proving OT algorithms. Several constraints have been proposed previously but they are generally difficult to follow and verify in practice. This paper proposes an alternative approach to address this problem. A new consistency model is defined which greatly simplifies the design and proof of OT algorithms
16976457
Fuzzy linear programming technique for multiattribute group decision making in fuzzy environments
The aim of this paper is to develop a linear programming technique for multidimensional analysis of preferences in multiattribute group decision making under fuzzy environments. Fuzziness is inherent in decision data and group decision making processes, and linguistic variables are well suited to assessing an alternative on qualitative attributes using fuzzy ratings. A crisp decision matrix can be converted into a fuzzy decision matrix once the decision makers' fuzzy ratings have been extracted. In this paper, we first define group consistency and inconsistency indices based on preferences to alternatives given by decision makers and construct a linear programming decision model based on the distance of each alternative to a fuzzy positive ideal solution which is unknown. Then the fuzzy positive ideal solution and the weights of attributes are estimated using the new decision model based on the group consistency and inconsistency indices. Finally, the distance of each alternative to the fuzzy positive ideal solution is calculated to determine the ranking order of all alternatives. A numerical example is examined to demonstrate the implementation process of the technique.
15275424
Blur identification based on kurtosis minimization
In this paper, we describe an algorithm for identifying a parametrically described blur based on kurtosis minimization. Using different choices for the parameters of the blur, the noisy blurred image is restored using Wiener filter. We use the kurtosis as a measurement of the quality of the restored image. From the set of the candidate deblurred images, the one with the minimum kurtosis is selected. The proposed technique is tested in a simulated experiment on a variety of blurs including atmospheric turbulence blurs, Gaussian blurs, and out-of-focus blurs. The proposed approach is also tested on real blurred images. Moreover, we test the performance when a wrong blur model is given. Our experiments show that the kurtosis minimization measurements match well with methods that maximize PSNR.