In this paper we develop approximation algorithms for two-stage convex chance constrained problems. Nemirovski and Shapiro [18] formulated this class of problems and proposed an ellipsoid-like iterative algorithm for the special case where the impact function f(x,h) is bi-affine. We show that this algorithm extends to bi-convex f(x,h) in a fairly straightforward fashion. The complexity of the s...

Analysis of inventory systems involving market-oriented pricing decisions has recently become an interesting topic in the field of inventory control. Price and marketing expenditure are considered as important elements when selling goods and enhancing revenues by manufacturers. The importance of accounting for uncertainty in such environments spurs an interest to develop appropriate decision ma...

Chance-constrained optimization (CCO) has been widely used for uncertainty management in power system operation. With the prevalence of wind energy, it becomes necessary to consider curtailment as a dispatch variable CCO. However, will cause an impulse distribution, yielding challenges explicit modeling chance constraint. In this paper, novel data-driven framework is developed handle issue. By ...

Particle candidates for astrophysical dark matter are reviewed, with particular emphasis on the lightest supersymmetric particle and the axion. The former is now constrained by accelerator experiments to have a mass above about 40 GeV, and ongoing searches at accelerators, in space, and in underground experiments have a good chance to detect it. A reevaluation of the constraint on the axion fro...

We consider the problem of preference-based search for configurable products. We focus on example-based tools that interactively show both candidates (best options given current preferences) and suggestions to stimulate preference expression. Suggestions are generated to maximize the chance of eliciting further preferences using our lookahead principle. We present Probabilistic Constraint Optim...

the realization optimality theory is a recent development in the original optimality theory which is proposed to deal with morphological issues especially the inflectional ones. its main idea is to consider the morphological realization rules as ranked violable language-specific constraints that control the realization processes and provide phonological information of grammatical morphemes. thi...

In chance-constrained optimization problems, a solution is assumed to be feasible only with certain, sufficiently high probability. For computational and theoretical purposes, the convexity property of the resulting constraint set is treated. It is known, for example, that a suitable combination of a concavity property of the probability distribution and concavity of constraint mappings are suf...

We study the discrete optimization problem under the distributionally robustframework. We optimize the Entropic Value-at-Risk, which is a coherentrisk measure and is also known as Bernstein approximation for the chanceconstraint. We propose an efficient approximation algorithm to resolve theproblem via solving a sequence of nominal problems. The computationalresults show...

We consider the problem of preference-based search for configurable products. We focus on example-based tools that interactively show both candidates (best options given current preferences) and suggestions to stimulate preference expression. Suggestions are generated to maximize the chance of eliciting further preferences using our lookahead principle. We present Probabilistic Constraint Optim...

Optimal allocation of sample size among various strata is an important step to get the precise estimates for population parameters and to reduce the cost of the survey. A reasonable criterion for optimal allocation is the minimization of the variances of the estimates for a specified cost or to minimize the cost of survey for desired precision of the estimates. The total cost of survey is a fun...