BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

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

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

Separable Programming: Theory and Methods (Applied Optimization, 53)
Stock photo: cover may vary

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

by Stefanov, S.M

Add to wish list
  • Used
  • Good
  • Hardback
Used - Good

Description

hardcover. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book.
Ask the seller a question Add to wish list
A$248.73
Free Delivery within USA
Standard delivery: 7 to 14 days
More delivery options
Dropship order
Ships from Bonita (California, United States)

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 # 0792368827.G
  • 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)
  • Category Mathematics
  • Library of Congress subjects Convex programming
  • Library of Congress Catalogue Number 2001029308
  • Dewey Decimal Code 519.76
  • Quantity available 1

About Bonita California, United States

Biblio member since 2020

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from Bonita

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.
tracking-