We propose a new, highly effective and easy-to-implement algorithm for solving large-scale mean-variance optimization … smaller than the number of assets, which is almost always the case. Our novel algorithm is built on the novel representation … of mean-variance models in terms of the support vector data description --- an unsupervised machine learning algorithm …

. Applying this algorithm to actual data, we find that TA is well-adapted to optimisation problems of this type. Furthermore, we …

Portfolio Theory, which is mainly focused on portfolio risk, introduced a new idea for asset diversification in portfolio … portfolio. In the first stage, Pareto efficient portfolios are obtained by genetic algorithm with using mean and variance of …

returns where optimization problems can be reduced to linear programs. The first algorithm solves the simple recourse problem … as described by Klein Haneveld and Van der Vlerk using Benders decomposition method. The second algorithm finds an …

approximation algorithm …