
Network design for st effective resistance
We consider a new problem of designing a network with small st effectiv...
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Efficient Algorithms for Measuring the Funnellikeness of DAGs
Funnels are a new natural subclass of DAGs. Intuitively, a DAG is a funn...
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Hedge Connectivity without Hedge Overlaps
Connectivity is a central notion of graph theory and plays an important ...
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A PolynomialTime Approximation Scheme for Facility Location on Planar Graphs
We consider the classic Facility Location problem on planar graphs (non...
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Improved Approximation for NodeDisjoint Paths in Grids with Sources on the Boundary
We study the classical NodeDisjoint Paths (NDP) problem: given an undir...
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Maximum Weight Disjoint Paths in Outerplanar Graphs via SingleTree Cut Approximators
Since 2000 there has been a steady stream of advances for the maximum we...
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GenFloor: Interactive Generative Space Layout System via Encoded Tree Graphs
Automated floorplanning or space layout planning has been a longstandin...
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A Simple Approximation for a Hard Routing Problem
We consider a routing problem which plays an important role in several applications, primarily in communication network planning and VLSI layout design. The original underlying graph algorithmic task is called Disjoint Paths problem. In most applications, one can encounter its capacitated generalization, which is known as the Unsplitting Flow problem. These algorithmic tasks are very hard in general, but various efficient (polynomialtime) approximate solutions are known. Nevertheless, the approximations tend to be rather complicated, often rendering them impractical in large, complex networks. Our goal is to present a solution that provides a simple, efficient algorithm for the unsplittable flow problem in large directed graphs. The simplicity is achieved by sacrificing a small part of the solution space. This also represents a novel paradigm of approximation: rather than giving up finding an exact solution, we restrict the solution space to its most important subset and exclude those that are marginal in some sense. Then we find the exact optimum efficiently within the subset. Specifically, the sacrificed parts (i.e., the marginal instances) only contain scenarios where some edges are very close to saturation. Therefore, the excluded part is not significant, since the excluded almost saturated solutions are typically undesired in practical applications, anyway.
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