This paper reviews the interplay between global optimization and probability models, concentrating on a class of deterministic optimization algorithms that are motivated by probability models for the objective function. Some complexity results are described for the univariate and multivariate cases.

In this paper, we propose a new method for estimating the shape of a surface from visual input. Assuming a parametric model of a surface, the parameters best explaining the perspective changes of the surface between different views are estimated. This is in contrast to the usual approach of fitting a model into a 3-D point cloud, generated by some previously calculated local correspondence matc...

In multivariate systems, when it comes to identifying actual operating conditions ranges, or optimal settings, the use of constrained optimization is often required. Among the different tools for the engineer to perform such optimization, designed experiments offer accurate performances. In this paper, the optimization process of ‘‘electroacoustic absorbers” is investigated by means of response...

The use of constraint optimization has recently proven to be a successful approach to providing solutions to various NP-hard search and optimization problems in data analysis. In this work we extend the use of constraint optimization systems further within data analysis to a central problem arising from the analysis of multivariate data, namely, determining minimum-width multivariate confidence...

For many decision making problems under uncertainty, it is crucial to develop risk-averse models and specify the decision makers’ risk preferences based on multiple stochastic performance measures (or criteria). Incorporating such multivariate preference rules into optimization models is a fairly recent research area. Existing studies focus on extending univariate stochastic dominance rules to ...