简介：苏循华，中国科学技术大学学士，挪威商学院硕士，挪威澳门新葡萄京金融博士，挪威科学技术大学博士后。现任挪威澳门新葡萄京金融系教授，苏教授具体研究方向为Fintech和公司金融，包括企业融资、创新、流动性管理、金融契约与企业竞争等。企业在当今社会扮演着极其重要的角色，针对企业竞争、融资、创新等方面的学术研究具有十分重要的理论与现实意义，其研究结果发表在顶级金融学杂志Journal of Financial and Quantitative Analysis (JFQA)，Journal of Banking and Finance, Journal of Corporate Finance, Journal of Money Credit and Banking (JMCB)，Financial Management等。
教授观点：Models of reduced computational complexity and guaranteed accuracy is indispensable in scenarios where a large number of numerical solutions to a sequence of problems are desired in a fast/real-time fashion. Reduced basis method (RBM) is such a paradigm in computational mathematics that can improve efficiency by several orders of magnitudes leveraging a machine learning philosophy, an offline-online procedure, and the recognition that the solution space of the concerned sequence of problems can be well approximated by a smaller space in a tailored fashion. A critical ingredient to guarantee the accuracy of the surrogate solution and guide the construction of the surrogate space is a mathematically rigorous theory.
After a brief introduction of RBM, this talk will present some of our recent applications including to fast face recognition, and a new fast iterative linear solver. Applications in economics and finance will be discussed as well.