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This book on optimization includes forewords by Michael I. Jordan,
Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on
optimization to solve problems with its learning models, and
first-order optimization algorithms are the mainstream approaches.
The acceleration of first-order optimization algorithms is crucial
for the efficiency of machine learning. 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. 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.
Offering a rich blend of ideas, theories and proofs, the book is
up-to-date and self-contained. 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.
This book on optimization includes forewords by Michael I. Jordan,
Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on
optimization to solve problems with its learning models, and
first-order optimization algorithms are the mainstream approaches.
The acceleration of first-order optimization algorithms is crucial
for the efficiency of machine learning. 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. 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.
Offering a rich blend of ideas, theories and proofs, the book is
up-to-date and self-contained. 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.
Machine learning heavily relies on optimization algorithms to solve
its learning models. Constrained problems constitute a major type
of optimization problem, and the alternating direction method of
multipliers (ADMM) is a commonly used algorithm to solve
constrained problems, especially linearly constrained ones. Written
by experts in machine learning and optimization, this is the first
book providing a state-of-the-art review on ADMM under various
scenarios, including deterministic and convex optimization,
nonconvex optimization, stochastic optimization, and distributed
optimization. Offering a rich blend of ideas, theories and proofs,
the book is up-to-date and self-contained. It is an excellent
reference book for users who are seeking a relatively universal
algorithm for constrained problems. Graduate students or
researchers can read it to grasp the frontiers of ADMM in machine
learning in a short period of time.
Machine learning heavily relies on optimization algorithms to solve
its learning models. Constrained problems constitute a major type
of optimization problem, and the alternating direction method of
multipliers (ADMM) is a commonly used algorithm to solve
constrained problems, especially linearly constrained ones. Written
by experts in machine learning and optimization, this is the first
book providing a state-of-the-art review on ADMM under various
scenarios, including deterministic and convex optimization,
nonconvex optimization, stochastic optimization, and distributed
optimization. Offering a rich blend of ideas, theories and proofs,
the book is up-to-date and self-contained. It is an excellent
reference book for users who are seeking a relatively universal
algorithm for constrained problems. Graduate students or
researchers can read it to grasp the frontiers of ADMM in machine
learning in a short period of time.
In this book, the authors present current research in the study of
the crystallography, chemistry and catalytic performance of
perovskites. Topics discussed include the defect structure and
defect-induced expansion of perovskite oxides; perovskite-based
catalysts for transformation of natural gas and oxygenates into
syngas; Bi containing multiferroic perovskite oxide thin films;
perovskites as catalysts for environmental remediation;
microwave-assisted synthesis and characterisation of perovskite
oxides; perovskite and lead based ceramic materials; photocatalytic
properties of perovskite-type layered oxides; structure of
perovskite electron-ionic conductors; and distorted perovskites.
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