Sinopsis
A guide to optimization with stochastic models, featuring both fundamental theory, and new material to reflect recent advances.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca de los autores
Alexander Shapiro is a Professor in the School of Industrial and Systems Engineering at Georgia Institute of Technology. He has published more than 100 articles and is the co-author of several books.
Darinka Dentcheva is a Professor of Mathematics at Stevens Institute of Technology. She works in the areas of decisions under uncertainty, convex analysis, and stability of optimization problems.
Andrzej Ruszczyński is a Professor of Operations Research at Rutgers University. His research is devoted to the theory and methods of optimization under uncertainty and risk.
"Sobre este título" puede pertenecer a otra edición de este libro.
Resultados de la búsqueda para Lectures on Stochastic Programming: Modeling and Theory
Imagen de archivo
Lectures on Stochastic Programming (Hardcover)
Publicado por
Society for Industrial & Applied Mathematics,U.S., New York, 2014
ISBN 10: 1611973422
ISBN 13: 9781611973426
Nuevo
Tapa dura
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Hardcover. Condición: new. Hardcover. Optimization problems involving stochastic models occur in most areas of science and engineering, particularly telecommunications, medicine, and finance. Their existence reveals a need for rigorous ways of formulating, analyzing, and solving such problems. This book focuses on optimization problems involving uncertain parameters and covers the theoretical foundations and recent advances in areas where stochastic models are available. In this second edition, the authors introduce new material to reflect recent developments, including: analytical descriptions of the tangent and normal cones of chance constrained sets; analysis of optimality conditions for nonconvex problems; a discussion of the stochastic dual dynamic programming method; an extended discussion of law invariant coherent risk measures and their Kusuoka representations; and an in-depth analysis of dynamic risk measures and concepts of time consistency, including several new results. This book is intended for researchers working in optimization. It is also suitable for advanced graduate courses in this area. Optimization problems involving stochastic models occur in most areas of science, revealing a need for rigorous ways of analyzing and solving such problems. This new edition focuses on theoretical foundations and recent developments, including new material on optimality conditions for nonconvex problems, law invariant coherent risk measures, and more. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781611973426
Comprar nuevo
EUR 116,82
Gastos de envío:
GRATIS
A Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen de archivo
Lectures on Stochastic Programming (Hardcover)
Publicado por
Society for Industrial & Applied Mathematics,U.S., New York, 2014
ISBN 10: 1611973422
ISBN 13: 9781611973426
Nuevo
Tapa dura
Librería: AussieBookSeller, Truganina, VIC, Australia
Calificación del vendedor: 5 de 5 estrellas
Hardcover. Condición: new. Hardcover. Optimization problems involving stochastic models occur in most areas of science and engineering, particularly telecommunications, medicine, and finance. Their existence reveals a need for rigorous ways of formulating, analyzing, and solving such problems. This book focuses on optimization problems involving uncertain parameters and covers the theoretical foundations and recent advances in areas where stochastic models are available. In this second edition, the authors introduce new material to reflect recent developments, including: analytical descriptions of the tangent and normal cones of chance constrained sets; analysis of optimality conditions for nonconvex problems; a discussion of the stochastic dual dynamic programming method; an extended discussion of law invariant coherent risk measures and their Kusuoka representations; and an in-depth analysis of dynamic risk measures and concepts of time consistency, including several new results. This book is intended for researchers working in optimization. It is also suitable for advanced graduate courses in this area. Optimization problems involving stochastic models occur in most areas of science, revealing a need for rigorous ways of analyzing and solving such problems. This new edition focuses on theoretical foundations and recent developments, including new material on optimality conditions for nonconvex problems, law invariant coherent risk measures, and more. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9781611973426
Comprar nuevo
EUR 176,58
Gastos de envío:
EUR 31,98
De Australia a Estados Unidos de America
Cantidad disponible: 1 disponibles