An interior-point solver for large structured optimization and (data science and alternative) applications

This is a talk given by Prof Jordi Castro as part of the Online Seminar Series Machine Learning NeEDS Mathematical Optimization, https://congreso.us.es/mlneedsmo/, branding the role of Operations Research in Artificial Intelligence with the support of EURO, https://www.euro-online.org.

Abstract:

Interior point methods (IPMs) have shown to behave very well in some classes of large structured optimization problems. We will discuss a successful approach for block-angular structures that relies on the efficient solution of the Newton direction. In the first part of the talk we will outline such specialized IPM, which is implemented in a solver named BlockIP. In the second part of the talk we will overview a few (data science an alternative) applications where this algorithm outperformed some of the state-of-the-art solvers, namely: (1) support vector machines; (2) statistical tabular data confidentiality; (3) multistatge stochastic optimization; (4) minimum convex cost flows in bipartite networks.

More information

Link to the talk: https://eu.bbcollab.com/guest/e80c02d25f9b489f8f3c6e644edb9c3d

Organisers

The organizers of the Online Seminar Series Machine Learning NeEDS Mathematical Optimization
Emilio Carrizosa, IMUS-Instituto de Matemáticas de la Universidad de Sevilla
Nuria Gómez-Vargas, IMUS-Instituto de Matemáticas de la Universidad de Sevilla
Thomas Halskov, Copenhagen Business School
Dolores Romero Morales, Copenhagen Business School