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\text{maximize } & \sum_{i=1}^m\sum_{j=1}^n p_{ij} x_{ij}. \\
\text{subject to } & \sum_{j=1}^n w_{ij} x_{ij} \le t_i & & i=1, \ldots, m; \\
& \sum_{i=1}^m x_{ij}
& x_{ij} \in \{0,1\} & & i=1, \ldots, m, \quad j=1, \ldots, n;
\end{align}
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== Complexity ==
The generalized assignment problem is [[NP-hard]]
| last1 = Özbakir | first1 = Lale
| last2 = Baykasoğlu | first2 = Adil
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| volume = 215
| issue = 11
| year = 2010}}.</ref> However, there are linear-programming relaxations which give a <math>(1 - 1/e)</math>-approximation.<ref>{{cite
==Greedy approximation algorithm==
For the problem variant in which not every item must be assigned to a bin, there is a family of algorithms for solving the GAP by using a combinatorial translation of any algorithm for the knapsack problem into an approximation algorithm for the GAP.<ref>{{cite journal |doi=10.1016/j.ipl.2006.06.003|title=An efficient approximation for the Generalized Assignment Problem|journal=Information Processing Letters|volume=100|issue=4|pages=162–166|year=2006|last1=Cohen|first1=Reuven|last2=Katzir|first2=Liran|last3=Raz|first3=Danny|citeseerx=10.1.1.159.1947}}</ref>
Using any <math>\alpha</math>-approximation algorithm ALG for the [[knapsack problem]], it is possible to construct a (<math>\alpha + 1</math>)-approximation for the generalized assignment problem in a greedy manner using a residual profit concept.
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==Further reading==
* {{cite book |isbn=978-3-540-24777-7|title=Knapsack Problems|last1=Kellerer|first1=Hans|last2=Pferschy|first2=Ulrich|last3=Pisinger|first3=David|date=2013-03-19|publisher=Springer }}
[[Category:NP-complete problems]]
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