Maximin and maximal solutions for linear programming problems with possibilistic uncertainty

We consider linear programming problems with uncertain constraint coefficients described by intervals or, more generally, possi-bility distributions. The uncertainty is given a behavioral interpretation using coherent lower previsions from the theory of imprecise probabilities. We give a meaning to the linear programming problems by reformulating them as decision problems under such imprecise-probabilistic uncer-tainty. We provide expressions for and illustrations of the maximin and maximal solutions of these decision problems and present computational approaches for dealing with them

A Polynomial Optimization Approach to Constant Rebalanced Portfolio Selection

We address the multi-period portfolio optimization problem with the constant rebalancing strategy. This problem is formulated as a polynomial optimization problem (POP) by using a mean-variance criterion. In order to solve the POPs of high degree, we develop a cutting-plane algorithm based on semidefinite programming. Our algorithm can solve problems that can not be handled by any of known polynomial optimization solvers.

Nonlinear Integer Programming

Research efforts of the past fifty years have led to a development of linear integer programming as a mature discipline of mathematical optimization. Such a level of maturity has not been reached when one considers nonlinear systems subject to integrality requirements for the variables. This chapter is dedicated to this topic. The primary goal is a study of a simple version of general nonlinear integer problems, where all constraints are still linear. Our focus is on the computational complexity of the problem, which varies significantly with the type of nonlinear objective function in combination with the underlying combinatorial structure. Numerous boundary cases of complexity emerge, which sometimes surprisingly lead even to polynomial time algorithms. We also cover recent successful approaches for more general classes of problems. Though no positive theoretical efficiency results are available, nor are they likely to ever be available, these seem to be the currently most successful and interesting approaches for solving practical problems. It is our belief that the study of algorithms motivated by theoretical considerations and those motivated by our desire to solve practical instances should and do inform one another. So it is with this viewpoint that we present the subject, and it is in this direction that we hope to spark further research.Comment: 57 pages. To appear in: M. J\"unger, T. Liebling, D. Naddef, G. Nemhauser, W. Pulleyblank, G. Reinelt, G. Rinaldi, and L. Wolsey (eds.), 50 Years of Integer Programming 1958--2008: The Early Years and State-of-the-Art Surveys, Springer-Verlag, 2009, ISBN 354068274

Trust-Based Scenarios – Predicting Future Agent Behavior in Open Self-organizing Systems

International audienceAgents in open self-organizing systems have to cope with a variety of uncertainties. In order to increase their utility and to ensure stable operation of the overall system, they have to capture and adapt to these uncertainties at runtime. This can be achieved by formulating an expectancy of the behavior of others and the environment. Trust has been proposed as a concept for this purpose.In this paper, we present trust-based scenarios as an enhancement of current trust models. Trust-based scenarios represent stochastic models that allow agents to take different possible developments of the environment’s or other agents’ behavior into account. We demonstrate that trust-based scenarios significantly improve the agents’ capability to predict future behavior with a distributed power management application

Author manuscript, published in "16th Int. Conf. on Principles and Practices of Constraint Programming (CP'2010) (2010)" On Testing Constraint Programs

Abstract. The success of several constraint-based modeling languages such as OPL, ZINC, or COMET, appeals for better software engineering practices, particularly in the testing phase. This paper introduces a testing framework enabling automated test case generation for constraint programming. We propose a general framework of constraint program development which supposes that a first declarative and simple constraint model is available from the problem specifications analysis. Then, this model is refined using classical techniques such as constraint reformulation, surrogate and global constraint addition, or symmetry-breaking to form an improved constraint model that must be thoroughly tested before being used to address real-sized problems. We think that most of the faults are introduced in this refinement step and propose a process which takes the first declarative model as an oracle for detecting non-conformities. We derive practical test purposes from this process to generate automatically test data that exhibit non-conformities. We implemented this approach in a new tool called CPTEST that was used to automatically detect non-conformities on two classical benchmark programs, namely the Golomb rulers and the car-sequencing problem

Paclitaxel delivery from PLGA foams for controlled release in post-surgical chemotherapy against glioblastoma multiforme

10.1016/j.biomaterials.2009.02.030Biomaterials30183189-3196BIMA

In vivo performance of implantable biodegradable preparations delivering Paclitaxel and Etanidazole for the treatment of glioma

10.1016/j.biomaterials.2006.09.044Biomaterials285886-894BIMA