AbstractIn this paper, we review convex mixed‐integer quadratic programming approaches to deal with single‐objective single‐period mean‐variance portfolio selection problems under real‐world financial constraints. In the first part, after describing the original Markowitz's mean‐variance model, we analyze its theoretical and empirical limitations, and summarize the possible improvements by considering robust and probabilistic models, and additional constraints. Moreover, we report some recent theoretical convexity results for the probabilistic portfolio selection problem. In the second part, we overview the exact algorithms proposed to solve the single‐objective single‐period portfolio selection problem with quadratic risk measure.