Robust optimization (RO) is a young and active research field that has been mainly developed in the last 15 years. RO techniques are very useful for practice and not difficult to understand for practitioners. It is therefore remarkable that real-life applications of RO are still lagging behind;...

We introduce a novel scheme based on a blending of Fourier-Motzkin elimination (FME) and adjustable robust optimization techniques to compute the maximum volume inscribed ellipsoid (MVE) in a polytopic projection. It is well-known that deriving an explicit description of a projected polytope is...

We derive sufficient conditions for non-emptyness of the efficient set for Stochastic Dominance Relations, commonly applied in Economics and Finance, over sets of distributions on the real line. We do so via the use of the concept of stochastic spanning and its characterization via a saddle type...

We develop and implement methods for determining whether introducing new securities or relaxing investment constraints improves the investment opportunity set for prospect investors. We formulate a new testing procedure for prospect spanning for two nested portfolio sets based on subsampling and...

Many optimization problems that arise in practice can be reduced to the problem of computing the projection of a given vector in a Euclidean space onto the simplicial cone generated by a set of linearly independent vectors. For example, the well-known problem in finance of determining the...

We consider dynamic asset allocation problems where the agent is required to pay capital gains taxes on her investment gains. This is a very challenging problem because the tax owed whenever a security is sold depends on the cost-basis, i.e. the price(s) at which the shares of the security was...