Special issue: Optimization models and algorithms for data science
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In the modern information age, data and decisions are more strongly linked than ever before. The recent emergence of ‘data science’ as an interdisciplinary field that aims to distil insights from data and that informs data-driven decision-making bears witness to the importance of rigorous, evidence-based decision-making that crosses the boundaries of statistics, computer science, machine learning and optimization. This special issue focuses on some of the recent data science-related advances in optimization theory. The contributions to this special issue include optimization models and algorithms that deal with large data sets, as well as applications from business analytics and machine learning that have emerged in the last decades as data accumulates from multiple sources.
In the first paper (Max-Norm Optimization for Robust Matrix Recovery), Ethan X. Fang, Han Liu, Kim-Chuan Toh and Wen-Xin Zhou develop a new estimator for the matrix completion problem under arbitrary sampling...