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    An Optimization Primer
      • 8% KEDVEZMÉNY?

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      • Kiadói listaár EUR 85.59
      • Az ár azért becsült, mert a rendelés pillanatában nem lehet pontosan tudni, hogy a beérkezéskor milyen lesz a forint árfolyama az adott termék eredeti devizájához képest. Ha a forint romlana, kissé többet, ha javulna, kissé kevesebbet kell majd fizetnie.

        36 307 Ft (34 578 Ft + 5% áfa)
      • Kedvezmény(ek) 8% (cc. 2 905 Ft off)
      • Discounted price 33 402 Ft (31 812 Ft + 5% áfa)

    Beszerezhetőség

    Becsült beszerzési idő: A Prosperónál jelenleg nincsen raktáron, de a kiadónál igen. Beszerzés kb. 3-5 hét..
    A Prosperónál jelenleg nincsen raktáron.

    Why don't you give exact delivery time?

    A beszerzés időigényét az eddigi tapasztalatokra alapozva adjuk meg. Azért becsült, mert a terméket külföldről hozzuk be, így a kiadó kiszolgálásának pillanatnyi gyorsaságától is függ. A megadottnál gyorsabb és lassabb szállítás is elképzelhető, de mindent megteszünk, hogy Ön a lehető leghamarabb jusson hozzá a termékhez.

    A termék adatai:

    • Kiadás sorszáma 1st ed. 2021
    • Kiadó Springer
    • Megjelenés dátuma 2022. március 29.
    • Kötetek száma 1 pieces, Book

    • ISBN 9783030762742
    • Kötéstípus Keménykötés
    • Terjedelem676 oldal
    • Méret 254x178 mm
    • Súly 1641 g
    • Nyelv angol
    • Illusztrációk 100 Illustrations, black & white; 55 Illustrations, color
    • 458

    Kategóriák

    Rövid leírás:

    This richly illustrated book introduces the subject of optimization to a broad audience with a balanced treatment of theory, models and algorithms. Through numerous examples from statistical learning, operations research, engineering, finance and economics, the text explains how to formulate and justify models while accounting for real-world considerations such as data uncertainty. It goes beyond the classical topics of linear, nonlinear and convex programming and deals with nonconvex and nonsmooth problems as well as games, generalized equations and stochastic optimization.

    The book teaches theoretical aspects in the context of concrete problems, which makes it an accessible onramp to variational analysis, integral functions and approximation theory. More than 100 exercises and 200 fully developed examples illustrate the application of the concepts. Readers should have some foundation in differential calculus and linear algebra. Exposure to real analysiswould be helpful but is not prerequisite. 

    Több

    Hosszú leírás:

    This richly illustrated book introduces the subject of optimization to a broad audience with a balanced treatment of theory, models and algorithms. Through numerous examples from statistical learning, operations research, engineering, finance and economics, the text explains how to formulate and justify models while accounting for real-world considerations such as data uncertainty. It goes beyond the classical topics of linear, nonlinear and convex programming and deals with nonconvex and nonsmooth problems as well as games, generalized equations and stochastic optimization.

    The book teaches theoretical aspects in the context of concrete problems, which makes it an accessible onramp to variational analysis, integral functions and approximation theory. More than 100 exercises and 200 fully developed examples illustrate the application of the concepts. Readers should have some foundation in differential calculus and linear algebra. Exposure to real analysiswould be helpful but is not prerequisite. 



    ?The book is well written. The notation is concise, the terminology is consistent, and the mathematical soundness is guaranteed. ? I highly recommend this book to anyone who wants to explore what optimization has to offer ? . For academics it will be a very convenient resource to look up useful results presented in a clear and concise form ? . I enjoyed reading the book and learned a lot, and I think you will, too!? (Tim Hoheisel, SIAM Review, Vol. 66 (3), August, 2024)



    ?In the reviewer's opinion, this is an important book ? . a lot of applications are given, so on one hand the readers can benefit from deep insights into the mathematical background of optimization theory ? . This book, which as all books reflects the tastes of its authors, is a solid reference, not only for graduate students and postgraduate students, but also for all those researchers interested in recent developments of optimization theory and methods.? (Giorgio Giorgi, Mathematical Reviews, December, 2022)

    Több

    Tartalomjegyzék:

    Prelude.- Convex optimization.- Optimization under uncertainty.- Minimization problems.- Perturbation and duality.- Without convexity or smoothness.- Generalized Equations.- Risk modeling and sample averages.- Games and minsup problems.- Decomposition.

    Több
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