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Linear Programming Vol. 1 : A Modern Integrated Analysis

Linear Programming Vol. 1 : A Modern Integrated Analysis

Linear Programming Vol. 1 : A Modern Integrated Analysis Hardback - 1995 - 1995th Edition

by Romesh Saigal

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Springer, 1995. Hardcover. Very Good. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.Dust jacket quality is not guaranteed.
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Details

  • Title Linear Programming Vol. 1 : A Modern Integrated Analysis
  • Author Romesh Saigal
  • Binding Hardback
  • Edition number 1995th
  • Edition 1995
  • Condition Used - Very good
  • Pages 342
  • Volumes 1
  • Language ENG
  • Publisher Springer
  • Publication date 1995
  • Features Bibliography
  • Bookseller's Inventory # G0792396227I4N00
  • ISBN 9780792396222 / 0792396227
  • Weight 1.5 lbs (0.68 kg)
  • Dimensions 9.21 x 6.14 x 0.81 in (23.39 x 15.60 x 2.06 cm)
  • Category Mathematics
  • Library of Congress Catalogue Number 95034605
  • Dewey Decimal Code 519.72
  • Quantity available 1

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From the publisher

In Linear Programming: A Modern Integrated Analysis, both boundary (simplex) and interior point methods are derived from the complementary slackness theorem and, unlike most books, the duality theorem is derived from Farkas's Lemma, which is proved as a convex separation theorem. The tedium of the simplex method is thus avoided.
A new and inductive proof of Kantorovich's Theorem is offered, related to the convergence of Newton's method. Of the boundary methods, the book presents the (revised) primal and the dual simplex methods. An extensive discussion is given of the primal, dual and primal-dual affine scaling methods. In addition, the proof of the convergence under degeneracy, a bounded variable variant, and a super-linearly convergent variant of the primal affine scaling method are covered in one chapter. Polynomial barrier or path-following homotopy methods, and the projective transformation method are also covered in the interior point chapter. Besides the popular sparse Cholesky factorization and the conjugate gradient method, new methods are presented in a separate chapter on implementation. These methods use LQ factorization and iterative techniques.

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