The interplay between optimization and machine learning is one of the                  most important developments in modern computational science. Optimization                  formulations and methods are proving to be vital in designing algorithms to extract                  essential knowledge from huge volumes of data. Machine learning, however, is not                  simply a consumer of optimization technology but a rapidly evolving field that is                  itself generating new optimization ideas. This book captures the state of the art of                  the interaction between optimization and machine learning in a way that is                  accessible to researchers in both fields.Optimization approaches have enjoyed                  prominence in machine learning because of their wide applicability and attractive                  theoretical properties. The increasing complexity, size, and variety of today's                  machine learning models call for the reassessment of existing assumptions. This book                  starts the process of reassessment. It describes the resurgence in novel contexts of                  established frameworks such as first-order methods, stochastic approximations,                  convex relaxations, interior-point methods, and proximal methods. It also devotes                  attention to newer themes such as regularized optimization, robust optimization,                  gradient and subgradient methods, splitting techniques, and second-order methods.                  Many of these techniques draw inspiration from other fields, including operations                  research, theoretical computer science, and subfields of optimization. The book will                  enrich the ongoing cross-fertilization between the machine learning community and                  these other fields, and within the broader optimization community.

Optimization for Machine Learning (Neural Information Processing series) PDF

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