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In optimization theory, semi-infinite programming (SIP) is an optimization problem with a finite number of variables and an infinite number of constraints, or an infinite number of variables and a finite number of constraints. In the former case the constraints are typically parameterized.

Mathematical formulation of the problem

The problem can be stated simply as:

$\min _{x\in X}\;\;f(x)$

${\text{subject to: }}\$

$g(x,y)\leq 0,\;\;\forall y\in Y$

where

$f:R^{n}\to R$

$g:R^{n}\times R^{m}\to R$

$X\subseteq R^{n}$

$Y\subseteq R^{m}.$

SIP can be seen as a special case of bilevel programs (multilevel programming) in which the lower-level variables do not participate in the objective function.

Methods for solving the problem

In the meantime, see external links below for a complete tutorial.

Examples

In the meantime, see external links below for a complete tutorial.

See also

• Optimization
• Generalized semi-infinite programming (GSIP)

References

• Edward J. Anderson and Peter Nash, Linear Programming in Infinite-Dimensional Spaces, Wiley, 1987.
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• M. A. Goberna and M. A. López, Linear Semi-Infinite Optimization, Wiley, 1998.
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• David Luenberger (1997). Optimization by Vector Space Methods. John Wiley & Sons. ISBN 0-471-18117-X.
• Rembert Reemtsen and Jan-J. Rückmann (Editors), Semi-Infinite Programming (Nonconvex Optimization and Its Applications). Springer, 1998, ISBN 0-7923-5054-5, 1998

External links

• Description of semi-infinite programming from INFORMS (Institute for Operations Research and Management Science).
• A complete, free, open source Semi Infinite Programming Tutorial is available here from Elsevier as a pdf download from their Journal of Computational and Applied Mathematics, Volume 217, Issue 2, 1 August 2008, Pages 394–419

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