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The field of optimization has undergone tremendous growth in its applications in science, engineering, business and finance in the past few decades. The growth has accelerated in recent years with the advent of big data analytics where optimization forms the core engine for solving and analyzing the underlying models and problems of extracting meaningful information from available data for the purpose of better decision making or getting better insights into the data sources. Spurred by the application needs and motivated by new emerging models in machine learning and data analytics, optimization research (in theory and algorithms) has also undergone rapid transformation and progress in recent years. In particular, demands for fast algorithms to solve extremely large-scale optimization problems arising from big data analytics have spurred numerous exciting new research directions in optimization theory and algorithms. The latter in turn helps to shape the development of optimization models and techniques in machine and statistical learning.