An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
On the complexity of string folding
Discrete Applied Mathematics - Special volume on computational molecular biology
Protein folding in the hydrophobic-hydrophilic (HP) is NP-complete
RECOMB '98 Proceedings of the second annual international conference on Computational molecular biology
On the complexity of protein folding (abstract)
RECOMB '98 Proceedings of the second annual international conference on Computational molecular biology
Approximation algorithms for protein folding prediction
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Spatial codes and the hardness of string folding problems
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
An efficient context-free parsing algorithm
Communications of the ACM
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We present polynomial-time approximation algorithms for string folding problems over any finite alphabet. Our idea is the following: describe a class of feasible solutions by means of an ambiguous context-free grammar (i.e. there is a bijection between the set of parse trees and a subset of possible embeddings of the string); give a score to every production of the grammar, so that the total score of every parse tree (the sum of the scores of the productions of the tree) equals the score of the corresponding structure; apply a parsing algorithm to find the parse tree with the highest score, corresponding to the configuration with highest score among those generated by the grammar. Furthermore, we show how the same approach can be extended in order to deal with an infinite alphabet or different goal functions. In each case, we prove that our algorithm guarantees a performance ratio that depends on the size of the alphabet or, in case of an infinite alphabet, on the length of the input string, both for the two and three-dimensional problem. Finally, we show some experimental results for the algorithm, comparing it to other performance-guaranteed approximation algorithms.