Constrained optimization: Difference between revisions

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== Relation to constraint-satisfaction problems ==

The constrained-optimization problem (COP) is a significant generalization of the classic [[constraint-satisfaction problem]] (CSP) model.<ref>{{Citation|last1=Rossi|first1=Francesca|title=Chapter 1 – Introduction|date=2006-01-01|url=http://www.sciencedirect.com/science/article/pii/S1574652606800052|work=Foundations of Artificial Intelligence|volume=2|pages=3–12|editor-last=Rossi|editor-first=Francesca|series=Handbook of Constraint Programming|publisher=Elsevier|doi=10.1016/s1574-6526(06)80005-2|access-date=2019-10-04|last2=van Beek|first2=Peter|last3=Walsh|first3=Toby|editor2-last=van Beek|editor2-first=Peter|editor3-last=Walsh|editor3-first=Toby|url-access=subscription}}</ref> COP is a CSP that includes an ''objective function'' to be optimized. Many algorithms are used to handle the optimization part.

==General form==

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====Lagrange multiplier====

{{main|Lagrange multipliers}}

If the constrained problem has only equality constraints, the method of [[Lagrange multipliers]] can be used to convert it into an unconstrained problem whose number of variables is the original number of variables minusplus the original number of equality constraints. Alternatively, if the constraints are all equality constraints and are all linear, they can be solved for some of the variables in terms of the others, and the former can be substituted out of the objective function, leaving an unconstrained problem in a smaller number of variables.

===Inequality constraints===

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* [[Constraint programming]]

* [[Integer programming]]

* [[Metric projection]]

* [[Penalty method]]

* [[Superiorization]]