A geometric framework for solving subsequence problems in computational biology efficiently
T Bernholt, F Eisenbrand, T Hofmeister - Proceedings of the twenty-third …, 2007 - dl.acm.org
T Bernholt, F Eisenbrand, T Hofmeister
Proceedings of the twenty-third annual symposium on Computational geometry, 2007•dl.acm.orgIn this paper, we introduce the notion of a constrained Minkowski sumwhich for two (finite)
point-sets P, Q⊆ R2 and a set of k inequalities Ax≥ b is defined as the point-set (P⊕ Q)
Ax≥ b= x= p+ q|∈ P, q∈ Q,, Ax≥ b. We show that typical subsequenceproblems from
computational biology can be solved by computing a setcontaining the vertices of the
convex hull of an appropriatelyconstrained Minkowski sum. We provide an algorithm for
computing such a setwith running time O (N log N), where N=| P|+| Q| if k is fixed. For the …
point-sets P, Q⊆ R2 and a set of k inequalities Ax≥ b is defined as the point-set (P⊕ Q)
Ax≥ b= x= p+ q|∈ P, q∈ Q,, Ax≥ b. We show that typical subsequenceproblems from
computational biology can be solved by computing a setcontaining the vertices of the
convex hull of an appropriatelyconstrained Minkowski sum. We provide an algorithm for
computing such a setwith running time O (N log N), where N=| P|+| Q| if k is fixed. For the …
In this paper, we introduce the notion of a constrained Minkowski sumwhich for two (finite) point-sets P,Q⊆ R2 and a set of k inequalities Ax≥ b is defined as the point-set (P ⊕ Q)Ax≥ b= x = p+q | ∈ P, q ∈ Q, , Ax ≥ b. We show that typical subsequenceproblems from computational biology can be solved by computing a setcontaining the vertices of the convex hull of an appropriatelyconstrained Minkowski sum. We provide an algorithm for computing such a setwith running time O(N log N), where N=|P|+|Q| if k is fixed. For the special case (P⊕ Q)x1≥ β, where P and Q consistof points with integer x1-coordinates whose absolute values arebounded by O(N), we even achieve a linear running time O(N). Wethereby obtain a linear running time for many subsequence problemsfrom the literature and improve upon the best known running times forsome of them.The main advantage of the presented approach is that it provides a generalframework within which a broad variety of subsequence problems canbe modeled and solved.This includes objective functions and constraintswhich are even more complexthan the ones considered before.

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