On Estimation Algorithms vs Approximation Algorithms
In a combinatorial optimization problem, when given an input
instance, one seeks a feasible solution that optimizes the value
of the objective function. Many combinatorial optimization
problems are NP-hard. A way of coping with NP-hardness is by
considering approximation algorithms. These algorithms run in
polynomial time, and their performance is measured by their
approximation ratio: the worst case ratio between the value of the
solution produced and the value of the (unknown) optimal solution.
In some cases the design of approximation algorithms includes a
nonconstructive component. As a result, the algorithms become
estimation algorithms rather than approximation algorithms: they
allow one to estimate the value of the optimal solution, without
actually producing a solution whose value is close to optimal.
We shall present a few such examples, and discuss some open
questions.
Estimation Algorithms
Approximation Algorithms
Combinatorial Optimization
357-363
Regular Paper
Uriel
Feige
Uriel Feige
10.4230/LIPIcs.FSTTCS.2008.1767
Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license
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