"Synopsis: Most of the tasks in data science, machine learning, computational physics, and inverse problems can be cast into data-driven constrained optimization problems. Such optimization problems could involve nonlinear, non-differentiable, and non-convex objectives and constraints. Though the past decades have seen tremendous advances in both theories and computational algorithms for optimization, efficiently solving large-scale nonlinear, non-differentiable, and non-convex optimization problems, and quantifying the uncertainty in their solution remains challenging. On the other hand, the exponential increase in the quantity of measurements and data holds tremendous promise for data-driven scientific discoveries. However, much data remains unused as optimization---a systematic tool to infer knowledge from data---is unable to scale up to the quantity of data being generated. Thus, there is a critical need to develop computational and data scalable strategies to tackle the challenge of large-scale data-driven optimization problems in order to continue the pace of scientific discoveries and to promote the progress of science. Machine learning is related to data science, but it also needs special attention to knowledge based concepts and thus need algorithms that can handle large data and also produce usable knowledge. Efficient algorithms for machine learning will be an important part of this conference. In addition, many of the algorithms used for big data and machine learning rooted back to many traditional algorithms used for computational science. Thus, we will also bring in new and traditional computational physics into this workshop. This workshop will provide a venue for dialogue and synergies between theoreticians in optimization and the computational scientists in the field so that the gap between the researchers working on the fundamentals and those working on real-life applications can be reduced. "

### Organizers

Raymond Chan (CityU, HK),

Roland Glowinski (Houston),

Fiorella Sgallari (Bologna),

Xue-Cheng Tai (Baptist Univ),

Suhua Wei (Inst of Applied Physics and Computational Math),

Bui-Thanh Tan (UT Austin)

### Participants

最新人员表.pdf

### Schedule

数据日程表_更新V16(1).pdf

### Titles and Abstracts

Titles&abstracts for workshop on Jan 11.pdf