Articles liés à Optimization Concepts
Synopsis
This book provides a clear, modern, and application‑driven introduction to mathematical optimization―one of the central technologies underlying today’s data‑driven decision making. Designed for students and practitioners encountering optimization for the first time, the book develops core concepts through intuitive explanations, concrete examples, and algorithmic insights, while maintaining mathematical rigor where it matters.
Beginning with smooth unconstrained problems, the text builds a unified framework connecting calculus, convexity, and optimality conditions. It progresses naturally to constrained and nonsmooth optimization, introducing subgradients, normal cones, and the Karush–Kuhn–Tucker condition in an accessible and coherent manner. Along the way, readers learn how fundamental algorithms―such as gradient, Newton’s, and simplex methods―emerge from the underlying theory and how they behave in practice.
The book places strong emphasis on modeling, guiding readers from real‑world problems to optimization formulations and solution strategies. Topics include linear and quadratic programming, network and integer structures, and optimization under uncertainty. Applications are drawn from engineering, operations research, data science, machine learning, and analytics, illustrating how optimization serves as a unifying language across disciplines.
Written as lecture notes for a one‑semester introductory course, this book assumes only basic knowledge of calculus and linear algebra. It builds intuition, vocabulary, and mathematical comprehension, while largely avoiding formal proofs. Numerous worked examples, exercises, and fully solved problems support self‑study and classroom use.
This book is ideal for advanced undergraduate students, master’s students, and practitioners seeking a principled yet approachable introduction to optimization, as well as a foundation for further study in applied mathematics, operations research, and analytics.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Optimization Concepts
Edité par
Springer, Berlin, Springer Jan 2027, 2027
ISBN 10 : 3032368189
ISBN 13 : 9783032368188
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a clear, modern, and application driven introduction to mathematical optimization one of the central technologies underlying today s data driven decision making. Designed for students and practitioners encountering optimization for the first time, the book develops core concepts through intuitive explanations, concrete examples, and algorithmic insights, while maintaining mathematical rigor where it matters.Beginning with smooth unconstrained problems, the text builds a unified framework connecting calculus, convexity, and optimality conditions. It progresses naturally to constrained and nonsmooth optimization, introducing subgradients, normal cones, and the Karush Kuhn Tucker condition in an accessible and coherent manner. Along the way, readers learn how fundamental algorithms such as gradient, Newton s, and simplex methods emerge from the underlying theory and how they behave in practice.The book places strong emphasis on modeling, guiding readers from real world problems to optimization formulations and solution strategies. Topics include linear and quadratic programming, network and integer structures, and optimization under uncertainty. Applications are drawn from engineering, operations research, data science, machine learning, and analytics, illustrating how optimization serves as a unifying language across disciplines.Written as lecture notes for a one semester introductory course, this book assumes only basic knowledge of calculus and linear algebra. It builds intuition, vocabulary, and mathematical comprehension, while largely avoiding formal proofs. Numerous worked examples, exercises, and fully solved problems support self study and classroom use.This book is ideal for advanced undergraduate students, master s students, and practitioners seeking a principled yet approachable introduction to optimization, as well as a foundation for further study in applied mathematics, operations research, and analytics. 260 pp. Englisch. N° de réf. du vendeur 9783032368188
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Optimization Concepts
Edité par
Springer Verlag GmbH, 2026
ISBN 10 : 3032368189
ISBN 13 : 9783032368188
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. N° de réf. du vendeur 3672839405
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Optimization Concepts
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a clear, modern, and application driven introduction to mathematical optimization one of the central technologies underlying today s data driven decision making. Designed for students and practitioners encountering optimization for the first time, the book develops core concepts through intuitive explanations, concrete examples, and algorithmic insights, while maintaining mathematical rigor where it matters.Beginning with smooth unconstrained problems, the text builds a unified framework connecting calculus, convexity, and optimality conditions. It progresses naturally to constrained and nonsmooth optimization, introducing subgradients, normal cones, and the Karush Kuhn Tucker condition in an accessible and coherent manner. Along the way, readers learn how fundamental algorithms such as gradient, Newton s, and simplex methods emerge from the underlying theory and how they behave in practice.The book places strong emphasis on modeling, guiding readers from real world problems to optimization formulations and solution strategies. Topics include linear and quadratic programming, network and integer structures, and optimization under uncertainty. Applications are drawn from engineering, operations research, data science, machine learning, and analytics, illustrating how optimization serves as a unifying language across disciplines.Written as lecture notes for a one semester introductory course, this book assumes only basic knowledge of calculus and linear algebra. It builds intuition, vocabulary, and mathematical comprehension, while largely avoiding formal proofs. Numerous worked examples, exercises, and fully solved problems support self study and classroom use.This book is ideal for advanced undergraduate students, master s students, and practitioners seeking a principled yet approachable introduction to optimization, as well as a foundation for further study in applied mathematics, operations research, and analytics. N° de réf. du vendeur 9783032368188
Acheter neuf
EUR 76,26
Expédition à EUR 35
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Optimization Concepts
Edité par
Springer Jan 2027, 2027
ISBN 10 : 3032368189
ISBN 13 : 9783032368188
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides a clear, modern, and application¿driven introduction to mathematical optimizationone of the central technologies underlying today's datädriven decision making. Designed for students and practitioners encountering optimization for the first time, the book develops core concepts through intuitive explanations, concrete examples, and algorithmic insights, while maintaining mathematical rigor where it matters.Beginning with smooth unconstrained problems, the text builds a unified framework connecting calculus, convexity, and optimality conditions. It progresses naturally to constrained and nonsmooth optimization, introducing subgradients, normal cones, and the KarushKuhnTucker condition in an accessible and coherent manner. Along the way, readers learn how fundamental algorithmssuch as gradient, Newton's, and simplex methodsemerge from the underlying theory and how they behave in practice.The book places strong emphasis on modeling, guiding readers from real¿world problems to optimization formulations and solution strategies. Topics include linear and quadratic programming, network and integer structures, and optimization under uncertainty. Applications are drawn from engineering, operations research, data science, machine learning, and analytics, illustrating how optimization serves as a unifying language across disciplines.Written as lecture notes for a one¿semester introductory course, this book assumes only basic knowledge of calculus and linear algebra. It builds intuition, vocabulary, and mathematical comprehension, while largely avoiding formal proofs. Numerous worked examples, exercises, and fully solved problems support self¿study and classroom use.This book is ideal for advanced undergraduate students, master's students, and practitioners seeking a principled yet approachable introduction to optimization, as well as a foundation for further study in applied mathematics, operations research, and analytics.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 276 pp. Englisch. N° de réf. du vendeur 9783032368188
Acheter neuf
EUR 64,19
Expédition à EUR 60
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)