Efficient methods for large-scale structural optimization with application to machine learning

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Nowadays structural optimization problems frequently arise in engineering and sciences, especially in data analytics, machine learning and statistics. In big data era, these problems are often of high dimension and consist of large number of functions in their objective and/or constraints. This brings unprecedented challenges to traditional optimization methods. The objectives of this proposal are to propose efficient methods and develop software for solving them, and explore their applications in data analytics and machine learning.