On Estimation Algorithms versus 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.

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