Confidence level solutions for stochastic programming

We propose an alternative approach to stochastic programming based on Monte-Carlo sampling and stochastic gradient optimization. The procedure is by essence probabilistic and the computed solution is a random variable. The associated objective value is doubly random, since it depends on two outcomes: the event in the stochastic program and the randomized algorithm. We propose a solution concept in which the probability that the randomized algorithm produces a solution with an expected objective value departing from the optimal one by more than $\epsilon$ is small enough. We derive complexity bounds for this process. We show that by repeating the basic process on independent sample, one can significantly sharpen the complexity bounds

A Krill Oil Supplemented Diet Suppresses Hepatic Steatosis in High-Fat Fed Rats

Krill oil (KO) is a dietary source of n-3 polyunsaturated fatty acids, mainly represented by eicosapentaenoic acid and docosahexaenoic acid bound to phospholipids. The supplementation of a high-fat diet with 2.5% KO efficiently prevented triglyceride and cholesterol accumulation in liver of treated rats. This effect was accompanied by a parallel reduction of the plasma levels of triglycerides and glucose and by the prevention of a plasma insulin increase. The investigation of the molecular mechanisms of KO action in high-fat fed animals revealed a strong decrease in the activities of the mitochondrial citrate carrier and of the cytosolic acetyl-CoA carboxylase and fatty acid synthetase, which are both involved in hepatic de novo lipogenesis. In these animals a significant increase in the activity of carnitine palmitoyl-transferase I and in the levels of carnitine was also observed, suggesting a concomitant stimulation of hepatic fatty acid oxidation. The KO supplemented animals also retained an efficient mitochondrial oxidative phosphorylation, most probably as a consequence of a KO-induced arrest of the uncoupling effects of a high-fat diet. Lastly, the KO supplementation prevented an increase in body weight, as well as oxidative damage of lipids and proteins, which is often found in high-fat fed animals

Confidence level solutions for stochastic

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Parallel Implementation of a Central Decomposition Method for Solving Large-Scale Planning Problems

We use a decomposition approach to solve three types of realistic problems: blockangular linear programs arising in energy planning, Markov decision problems arising in production planning and multicommodity network problems arising in capacity planning for survivable telecommunication networks. Decomposition is an algorithmic device that breaks down computations into several independent subproblems. It is thus ideally suited to parallel implementation. To achieve robustness and greater reliability in the performance of the decomposition algorithm, we use the Analytic Center Cutting Plane Method (accpm) to handle the master program. We run the algorithm on two different parallel computing platforms: a network of PC's running under Linux and a genuine parallel machine, the IBM SP2. The approach is well adapted for this coarse grain parallelism and the results display good speed-up's for the classes of problems we have treated. Keywords Decomposition, Parallel computation, Analytic cent..

Homogeneous Analytic Center Cutting Plane Methods for Convex Problems and Variational Inequalities.

In this paper we consider a new analytic center cutting plane method in a projective space. We prove the efficiency for the general scheme and show that these results can be used in the analysis of a feasability problem, the variational inequality problem and the problem if constrained minimization. Our analysis is valid even for the problems whose solution belongs to the boundary of the domain.EVALUATION