Processing second-order stochastic dominance models using cutting-plane representations

This is the post-print version of the Article. The official published version can be accessed from the links below. Copyright @ 2011 Springer-VerlagSecond-order stochastic dominance (SSD) is widely recognised as an important decision criterion in portfolio selection. Unfortunately, stochastic dominance models are known to be very demanding from a computational point of view. In this paper we consider two classes of models which use SSD as a choice criterion. The first, proposed by Dentcheva and Ruszczyński (J Bank Finance 30:433–451, 2006), uses a SSD constraint, which can be expressed as integrated chance constraints (ICCs). The second, proposed by Roman et al. (Math Program, Ser B 108:541–569, 2006) uses SSD through a multi-objective formulation with CVaR objectives. Cutting plane representations and algorithms were proposed by Klein Haneveld and Van der Vlerk (Comput Manage Sci 3:245–269, 2006) for ICCs, and by Künzi-Bay and Mayer (Comput Manage Sci 3:3–27, 2006) for CVaR minimization. These concepts are taken into consideration to propose representations and solution methods for the above class of SSD based models. We describe a cutting plane based solution algorithm and outline implementation details. A computational study is presented, which demonstrates the effectiveness and the scale-up properties of the solution algorithm, as applied to the SSD model of Roman et al. (Math Program, Ser B 108:541–569, 2006).This study was funded by OTKA, Hungarian National Fund for Scientific Research, project 47340; by Mobile Innovation Centre, Budapest University of Technology, project 2.2; Optirisk Systems, Uxbridge, UK and by BRIEF (Brunel University Research Innovation and Enterprise Fund)

Lipid droplet binding thalidomide analogs activate endoplasmic reticulum stress and suppress hepatocellular carcinoma in a chemically induced transgenic mouse model

BACKGROUND: Hepatocellular carcinoma (HCC) is the most frequent and aggressive primary tumor of the liver and it has limited treatment options. RESULTS: In this study, we report the in vitro and in vivo effects of two novel amino-trifluoro-phtalimide analogs, Ac-915 and Ac-2010. Both compounds bind lipid droplets and endoplasmic reticulum membrane, and interact with several proteins with chaperone functions (HSP60, HSP70, HSP90, and protein disulfide isomerase) as determined by affinity chromatography and resonant waveguide optical biosensor technology. Both compounds inhibited protein disulfide isomerase activity and induced cell death of different HCC cells at sub or low micromolar ranges detected by classical biochemical end-point assay as well as with real-time label-free measurements. Besides cell proliferation inhibiton, analogs also inhibited cell migration even at 250 nM. Relative biodistribution of the analogs was analysed in native tissue sections of different organs after administration of drugs, and by using fluorescent confocal microscopy based on the inherent blue fluorescence of the compounds. The analogs mainly accumulated in the liver. The effects of Ac-915 and Ac-2010 were also demonstrated on the advanced stages of hepatocarcinogenesis in a transgenic mouse model of N-nitrosodiethylamine (DEN)-induced HCC. Significantly less tumor development was found in the livers of the Ac-915- or Ac-2010-treated groups compared with control mice, characterized by less liver tumor incidence, fewer tumors and smaller tumor size. CONCLUSION: These results imply that these amino-trifluoro-phthalimide analogs could serve potent clinical candidates against HCC alone or in combination with dietary polyunsaturated fatty acids

Computational aspects of risk-averse optimizationin two-stage stochastic models

Computational studies on two-stage stochastic programming problems indicate that aggregate models have better scale-up properties than disaggregate ones, though the threshold of breaking even may be high. In this paper we attempt to explain this phenomenon, and to lower this threshold.We present the on-demand accuracy approach of Oliveira and Sagastizábal in a form which shows that this approach, when applied to two-stage stochastic programming problems, combines the advantages of the disaggregate and the aggregate models.Moreover, we generalize the on-demand accuracy approach to constrained convex problems, and showhow to apply it to risk-averse two-stage stochastic programming problems

A randomized method for handling a difficult function in a convex optimization problem, motivated by probabilistic programming

We propose a randomized gradient method for the handling of a convex function whose gradient computation is demanding. The method bears a resemblance to the stochastic approximation family. But in contrast to stochastic approximation, the present method builds a model problem. The approach requires that estimates of function values and gradients be provided at the iterates. We present a variance reduction Monte Carlo simulation procedure to provide such estimates in the case of certain probabilistic functions

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aspects of risk-averse optimizatio

Handling CVaR objectives and constraints in two-stage stochastic models

Based on the polyhedral representation of Künzi-Bay and Mayer [Künzi-Bay, A., Mayer, J., 2006. Computational aspects of minimizing conditional value-at-risk. Computational Management Science 3, 3-27] , we propose decomposition frameworks for handling CVaR objectives and constraints in two-stage stochastic models. For the solution of the decomposed problems we propose special Level-type methods.