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

Thursday, February 6, 2020



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. This webinar introduces the background of SOCP and QCQP and reviews basic and more advanced modelling techniques which will be demonstrated on real-world examples in Portfolio Optimization.

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.


Mathematical Optimization Solutions

Mathematical Optimization, also known as Mathematical Programming, is an aid for decision making utilized on a grand scale across all industries. Advanced analytical techniques are used to find the best value of the inputs from a given set which is specified by physical limits of the problem and user's restrictions. The quality of the result is measured by a user metric provided as a scalar function of the inputs. Optimization problems come from a massively diverse range of fields and industries, such as portfolio optimization or calibration in finance, structural optimization in engineering, data fitting in weather forecasting, parameter estimation in chemistry and many more. Click here to learn more about NAG's Mathematical Optimization Solutions.