Nowadays there is a wide range of optimization solvers available. However, it is sometimes difficult to choose the best solver for your model to gain all the potential benefits. Convex optimization, particularly Second-order Cone Programming (SOCP) and Quadratically Constrained Quadratic Programming (QCQP), saw a massive increase of interest thanks to robustness and performance. A key issue is to recognize what models can be reformulated and solved this way. This webinar introduces the background of SOCP and QCQP, and reviews basic and more advanced modelling techniques. These techniques are demonstrated in real-world examples in Portfolio Optimization.