The dead-end elimination algorithm (DEE) is a method for minimizing a function over a discrete set of independent variables. The basic idea is to identify "dead ends", i.e., "bad" combinations of variables that cannot possibly yield the global minimum and to refrain from searching such combinations further. Hence, dead-end elimination is a mirror image of dynamic programming, in which "good" combinations are identified and explored further. Although the method itself is general, it has been developed and applied mainly to the problems of predicting and designing the structures of proteins. The original description and proof of the dead-end elimination theorem can be found in .
Read more about Dead-end Elimination: Basic Requirements, Applications To Protein Structure Prediction, Implementation and Efficiency, Protein Design, Generalizations
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