This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. p. cm. He is a Fellow of IAPR and IEEE. It seems that you're in USA. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. Accelerated Optimization for Machine Learning by Zhouchen Lin, Huan Li, Cong Fang, May 30, 2020, Springer edition, hardcover We have a dedicated site for USA. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or … He is currently an Assistant Professor at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. Abstract. Accelerated Optimization for Machine Learning First-Order Algorithms by Zhouchen Lin; Huan Li; Cong Fang and Publisher Springer. To meet the demands of big data applications, lots of efforts have been put on designing theoretically and practically fast algorithms. Ahead of Print. Optimization for machine learning / edited by Suvrit Sra, Sebastian Nowozin, and Stephen J. Wright. — (Neural information processing series) Includes bibliographical references. Traditional optimiza- tion algorithms used in machine learning are often ill-suited for distributed environments with high communication cost. Abstract Numerical optimization serves as one of the pillars of machine learning. The HPE deep machine learning portfolio is designed to provide real-time intelligence and optimal platforms for extreme compute, scalability & … Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. © 2020 Springer Nature Switzerland AG. Stochastic gradient descent (SGD) is the simplest optimization algorithm used to find parameters which minimizes the given cost function. (gross), © 2020 Springer Nature Switzerland AG. (2020) Variance-Reduced Methods for Machine Learning. This article provides a comprehensive survey on accelerated first-order algorithms with a focus on stochastic algorithms. Happy Holidays—Our $/£/€30 Gift Card just for you, and books ship free! Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. Lin, Zhouchen, Li, Huan, Fang, Cong. His current research interests include optimization and machine learning. First, a TO problem often involves a large number of design variables to guarantee sufficient expressive power. JavaScript is currently disabled, this site works much better if you It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or … Apparently, for gradient descent to converge to optimal minimum, cost function should be convex. Abstract: Numerical optimization serves as one of the pillars of machine learning. Proceedings of the IEEE 108 :11, 2067-2082. Integration Methods and Accelerated Optimization Algorithms. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. The goal for optimization algorithm is to find parameter values which correspond to minimum value of cost function… Please review prior to ordering, The first monograph on accelerated first-order optimization algorithms used in machine learning, Includes forewords by Michael I. Jordan, Zongben Xu, and Zhi-Quan Luo, and written by experts on machine learning and optimization, Is comprehensive, up-to-date, and self-contained, making it is easy for beginners to grasp the frontiers of optimization in machine learning, ebooks can be used on all reading devices, Institutional customers should get in touch with their account manager, Usually ready to be dispatched within 3 to 5 business days, if in stock, The final prices may differ from the prices shown due to specifics of VAT rules. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time. 81.3.23.50, Accelerated First-Order Optimization Algorithms, Key Lab. First-order optimization algorithms are very commonly... Understanding the Optimization landscape of deep neural networks. 2010 F. Bach. Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. of Machine Perception School of EECS, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, School of Engineering and Applied Science, https://doi.org/10.1007/978-981-15-2910-8, COVID-19 restrictions may apply, check to see if you are impacted, Accelerated Algorithms for Unconstrained Convex Optimization, Accelerated Algorithms for Constrained Convex Optimization, Accelerated Algorithms for Nonconvex Optimization. Therefore, SGD has been successfully applied to many large-scale machine learning problems [9,15,16], especially training deep network models [17]. He is currently a Postdoctoral Researcher at Princeton University. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Advances in Neural Information Processing Systems (NIPS), ... editors, Optimization for Machine Learning, MIT Press, 2011. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. Springer is part of, Please be advised Covid-19 shipping restrictions apply. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Authors: Topology optimization (TO) is a mathematical method that optimizes material layout within a given set of constraints with the goal of maximizing the performance of the system. Technical report, HAL 00527714, 2010. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. Different from size and shape optimization, TO, enables the creation, merging and splitting of the interior solids and voids during the structural evolution and therefore, a much larger design space can be explored. We start with introducing the accelerated methods for smooth problems with Lipschitz continuous gradients, then concentrate on the methods for composite problems and specially study the case when the proximal mapping and the gradient are inexactly … I. Sra, Suvrit, 1976– II. Over 10 million scientific documents at your fingertips. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or … We start with defining some random initial values for parameters. His research interests include machine learning and optimization. This work is enabled by over 15 years of CUDA development. 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