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In [[mathematical optimization]], '''constrained optimization''' (in some contexts called '''constraint optimization''') is the process of optimizing an objective function with respect to some [[variable (mathematics)|variables]] in the presence of [[Constraint (mathematics)|constraints]] on those variables. The objective function is either a [[Loss function|cost function]] or [[energy function]], which is to be [[Maxima and minima|minimized]], or a [[reward function]] or [[utility function]], which is to be [[maximize]]d. Constraints can be either '''hard constraints''', which set conditions for the variables that are required to be satisfied, or '''soft constraints''', which have some variable values that are penalized in the objective function if, and based on the extent that, the conditions on the variables are not satisfied.
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The constrained-optimization problem (COP) is a significant generalization of the classic [[constraint-satisfaction problem]] (CSP) model<ref>{{Citation|last=Rossi|first=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}}</ref>. COP is a CSP that includes an ''objective function'' to be optimized. Many algorithms are used to handle the optimization part.
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