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Separable Programming

af S. M. Stefanov

indgår i Applied Optimization serien

Separable Programming af S. M. Stefanov
Bag om Separable Programming

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.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9780792368823
  • Indbinding:
  • Hardback
  • Sideantal:
  • 340
  • Udgivet:
  • 31. maj 2001
  • Størrelse:
  • 160x23x241 mm.
  • Vægt:
  • 676 g.
  • 8-11 hverdage.
  • 10. december 2024
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Forlænget returret til d. 31. januar 2025

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Beskrivelse af Separable Programming

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.

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