<Para ID="Par1">We formulate an objective as a convex combination of expectation and risk, measured by the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\mathrm{CVaR }$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mi mathvariant="normal">CVaR</mi> </math> </EquationSource> </InlineEquation> risk measure. The poor performance of standard Monte Carlo estimators applied on functions of this form is discussed and a variance reduction scheme based on importance...</equationsource></equationsource></inlineequation></para>

In this thesis an algorithm (MLOPT) for mixed logit models is proposed. Mixed logit models are flexible discrete choice models, but their estimation with large datasets involves the solution of a nonlinear optimization problem with a high dimensional integral in the objective function, which is...

An efficient Monte Carlo method for random sample generation from high dimensional distributions of complex structures is developed. The method is based on random discretization of the sample space and direct inversion of the discretized cumulative distribution function. It requires only the...

We consider interstage dependent stochastic linear programs where both the random right-hand side and the model of the underlying stochastic process have a special structure. Namely, for equality constraints (resp. inequality constraints) the right-hand side is an affine function (resp. a given...