Results 1 
6 of
6
Reflections on multivariate algorithmics and problem parameterization
 PROC. 27TH STACS
, 2010
"... Research on parameterized algorithmics for NPhard problems has steadily grown over the last years. We survey and discuss how parameterized complexity analysis naturally develops into the field of multivariate algorithmics. Correspondingly, we describe how to perform a systematic investigation and e ..."
Abstract

Cited by 24 (19 self)
 Add to MetaCart
Research on parameterized algorithmics for NPhard problems has steadily grown over the last years. We survey and discuss how parameterized complexity analysis naturally develops into the field of multivariate algorithmics. Correspondingly, we describe how to perform a systematic investigation and exploitation of the “parameter space” of computationally hard problems.
Exploiting bounded signal flow for graph orientation based on causeeffect pairs
 In Proceedings of the 1st International ICST Conference on Theory and Practice of Algorithms in (Computer) Systems (TAPAS 2011
"... Background: We consider the following problem: Given an undirected network and a set of sender–receiver pairs, direct all edges such that the maximum number of “signal flows ” defined by the pairs can be routed respecting edge directions. This problem has applications in understanding protein intera ..."
Abstract

Cited by 7 (0 self)
 Add to MetaCart
Background: We consider the following problem: Given an undirected network and a set of sender–receiver pairs, direct all edges such that the maximum number of “signal flows ” defined by the pairs can be routed respecting edge directions. This problem has applications in understanding protein interaction based cell regulation mechanisms. Since this problem is NPhard, research so far concentrated on polynomialtime approximation algorithms and tractable special cases. Results: We take the viewpoint of parameterized algorithmics and examine several parameters related to the maximum signal flow over vertices or edges. We provide several fixedparameter tractability results, and in one case a sharp complexity dichotomy between a lineartime solvable case and a slightly more general NPhard case. We examine the value of these parameters for several realworld network instances. Conclusions: Several biologically relevant special cases of the NPhard problem can be solved to optimality. In this way, parameterized analysis yields both deeper insight into the computational complexity and practical solving strategies. Background Current technologies [1] like twohybrid screening can
Fast and Accurate Search for Noncoding RNA Pseudoknot Structures in Genomes
"... Motivation: Searching genomes for noncoding RNAs (ncRNAs) by their secondary structure has become an important goal for bioinformatics. For pseudoknotfree structures, ncRNA search can be effective based on the covariance model and CYKtype dynamic programming. However, the computational difficulty ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Motivation: Searching genomes for noncoding RNAs (ncRNAs) by their secondary structure has become an important goal for bioinformatics. For pseudoknotfree structures, ncRNA search can be effective based on the covariance model and CYKtype dynamic programming. However, the computational difficulty in aligning an RNA sequence to a pseudoknot has prohibited fast and accurate search of arbitrary RNA structures. Our previous work introduced a graph model for RNA pseudoknots and proposed to solve the structuresequence alignment by graph optimization. Given k candidate regions in the target sequence for each of the n stems in the structure, we could compute best alignment in time O(k t n) based upon a tree width t decomposition of the structure graph. However, to implement this method to programs that can routinely perform fast yet accurate RNA pseudoknot searches, we need novel heuristics to ensure that,
Intractability; FixedParameter Tractability
"... Abstract. Research on parameterized algorithmics for NPhard problems has steadily grown over the last years. We survey and discuss how parameterized complexity analysis naturally develops into the field of multivariate algorithmics. Correspondingly, we describe how to perform a systematic investiga ..."
Abstract
 Add to MetaCart
Abstract. Research on parameterized algorithmics for NPhard problems has steadily grown over the last years. We survey and discuss how parameterized complexity analysis naturally develops into the field of multivariate algorithmics. Correspondingly, we describe how to perform a systematic investigation and exploitation of the “parameter space ” of computationally hard problems.
On the Ordered List Subgraph Embedding Problems
"... Abstract. In the (parameterized) Ordered List Subgraph Embedding problem (pOLSE) we are given two graphs G and H, each with a linear order defined on its vertices, a function L that associates with every vertex in G a list of vertices in H, and a parameter k. The question is to decide if we can emb ..."
Abstract
 Add to MetaCart
Abstract. In the (parameterized) Ordered List Subgraph Embedding problem (pOLSE) we are given two graphs G and H, each with a linear order defined on its vertices, a function L that associates with every vertex in G a list of vertices in H, and a parameter k. The question is to decide if we can embed (onetoone) a subgraph S of G of cardinality k into H such that: (1) every vertex of S is mapped to a vertex from its associated list, (2) the linear orders inherited by S and its image under the embedding are respected, and (3) if there is an edge between two vertices in S then there is an edge between their images. If we require the subgraph S to be embedded as an induced subgraph, we obtain the Ordered List Induced Subgraph Embedding problem (pOLISE). The pOLSE and pOLISE problems model various problems in Bioinformatics related to structural comparison/alignment of proteins. We investigate the complexity of pOLSE and pOLISE with respect to the following structural parameters: the width ∆L of the function L (size of the largest list), and the maximum degree ∆H of H and ∆G of G. We provide tight characterizations of the classical and parameterized complexity, and approximability of the problems with respect to the structural parameters under consideration.