Modern modelling techniques in convex optimization and its applicability to finance and beyond

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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.

NAG’s first webinar of 2020 introduces the background of SOCP and QCQP, and reviews basic and more advanced modelling techniques. These techniques will be demonstrated in real-world examples in Portfolio Optimization.

Don’t worry if you can’t attend the live presentation, we will send you a copy of the recording.

About the Presenter:
Dr Shuanghua Bai became a developer in mathematical optimization in NAG after completing his PhD in conic programming from the University of Southampton in 2017. Since then he has been using his expertise in convex optimization to develop efficient and versatile solvers in the NAG Library.