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'Supply Chain 5.0: The Next Generation of Business Success through Customer Centricity, Sustainability & Human Rights and Digitalization'unveils a transformative era in supply chain management, presenting a groundbreaking business model that integrates three pivotal elements: Customer Centricity, Sustainability & Human Rights, and the interplay of Advanced Digitalization. This insightful book guides businesses through the evolving landscape, showcasing how they can synchronize these elements to revolutionize their operations and unlock unparalleled value, ensuring future success. As consumer expectations shift and the world demands responsible practices, embracing the new generation of Supply Chain isn't just an option — it's the only way for organizations to thrive and shape a profitable and sustainable future.

Non-convex Optimization for Machine Learning takes an in-depth look at the basics of non-convex optimization with applications to machine learning. It introduces the rich literature in this area, as well as equips the reader with the tools and techniques needed to apply and analyze simple but powerful procedures for non-convex problems. Non-convex Optimization for Machine Learning is as self-contained as possible while not losing focus of the main topic of non-convex optimization techniques. The monograph initiates the discussion with entire chapters devoted to presenting a tutorial-like treatment of basic concepts in convex analysis and optimization, as well as their non-convex counterparts. The monograph concludes with a look at four interesting applications in the areas of machine learning and signal processing, and exploring how the non-convex optimization techniques introduced earlier can be used to solve these problems. The monograph also contains, for each of the topics discussed, exercises and figures designed to engage the reader, as well as extensive bibliographic notes pointing towards classical works and recent advances. Non-convex Optimization for Machine Learning can be used for a semester-length course on the basics of non-convex optimization with applications to machine learning. On the other hand, it is also possible to cherry pick individual portions, such the chapter on sparse recovery, or the EM algorithm, for inclusion in a broader course. Several courses such as those in machine learning, optimization, and signal processing may benefit from the inclusion of such topics.