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.^[1]

Mathematical formulation of the problem

The problem can be stated simply as:

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

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

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

where

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

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

${\displaystyle X\subseteq R^{n}}$

${\displaystyle Y\subseteq R^{m}.}$

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

Methods for solving the problem

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

Examples

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

See also

• Optimization
• Generalized semi-infinite programming (GSIP)

References

External links

• Description of semi-infinite programming from INFORMS (Institute for Operations Research and Management Science).

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