苏循华 教授观点

2019-10-28

简介:苏循华,中国科学技术大学学士,挪威商学院硕士,挪威经济学院金融博士,挪威科学技术大学博士后。现任挪威经济学院金融系教授,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。

报告题目:Block chain

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.

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