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Separable Programming: Theory and Methods (Applied Optimization, 53)

Separable Programming: Theory and Methods (Applied Optimization, 53)

Separable Programming: Theory and Methods (Applied Optimization, 53)
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Separable Programming: Theory and Methods (Applied Optimization, 53) Hardback - 2001 - 2001st Edition

by Stefanov, S.M

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Springer, 2001-05-31. 2001. hardcover. Used: Good. 6.44x0.92x9.70. Buy with confidence. Excellent Customer Service & Return policy.
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Details

  • Title Separable Programming: Theory and Methods (Applied Optimization, 53)
  • Author Stefanov, S.M
  • Binding Hardback
  • Edition number 2001st
  • Edition 2001
  • Condition Used: Good
  • Pages 314
  • Volumes 1
  • Language ENG
  • Publisher Springer
  • Publication date 2001-05-31
  • Features Bibliography, Index
  • Bookseller's Inventory # SONG0792368827
  • ISBN 9780792368823 / 0792368827
  • Weight 1.42 lbs (0.64 kg)
  • Dimensions 9.7 x 6.44 x 0.92 in (24.64 x 16.36 x 2.34 cm)
  • Size 6.44x0.92x9.70
  • Category Mathematics
  • Library of Congress subjects Convex programming
  • Library of Congress Catalogue Number 2001029308
  • Dewey Decimal Code 519.76
  • Quantity available 1

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Reader reviews for Separable Programming: Theory and Methods (Applied Optimization, 53)

From the publisher

In this book, the author considers separable programming and, in particular, one of its important cases - convex separable programming. Some general results are presented, techniques of approximating the separable problem by linear programming and dynamic programming are considered.
Convex separable programs subject to inequality/ equality constraint(s) and bounds on variables are also studied and iterative algorithms of polynomial complexity are proposed.
As an application, these algorithms are used in the implementation of stochastic quasigradient methods to some separable stochastic programs. Numerical approximation with respect to I1 and I4 norms, as a convex separable nonsmooth unconstrained minimization problem, is considered as well.
Audience: Advanced undergraduate and graduate students, mathematical programming/ operations research specialists.

First line

In this chapter, wo give some definitions and results connected with convex analysis, convex programming, and Lagrangian duality.
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