8 research outputs found
Flow Computations on Imprecise Terrains
We study the computation of the flow of water on imprecise terrains. We
consider two approaches to modeling flow on a terrain: one where water flows
across the surface of a polyhedral terrain in the direction of steepest
descent, and one where water only flows along the edges of a predefined graph,
for example a grid or a triangulation. In both cases each vertex has an
imprecise elevation, given by an interval of possible values, while its
(x,y)-coordinates are fixed. For the first model, we show that the problem of
deciding whether one vertex may be contained in the watershed of another is
NP-hard. In contrast, for the second model we give a simple O(n log n) time
algorithm to compute the minimal and the maximal watershed of a vertex, where n
is the number of edges of the graph. On a grid model, we can compute the same
in O(n) time
Flow computations on imprecise terrains
Abstract. We study water flow computation on imprecise terrains. We consider two approaches to modeling flow on a terrain: one where water flows across the surface of a polyhedral terrain in the direction of steepest descent, and one where water only flows along the edges of a predefined graph, for example a grid or a triangulation. In both cases each vertex has an imprecise elevation, given by an interval of possible values, while its (x, y)-coordinates are fixed. For the first model, we show that the problem of deciding whether one vertex may be contained in the watershed of another is NP-hard. In contrast, for the second model we give a simple O(n log n) time algorithm to compute the minimal and the maximal watershed of a vertex, or a set of vertices, where n is the number of edges of the graph. On a grid model, we can compute the same in O(n) time. Rose knew almost everything that water can do, there are an awful lot when you think what. Gertrude Stein, The World is Round
Flow computations on imprecise terrains
We study water flow computation on imprecise terrains. We
consider two approaches to modeling flow on a terrain: one where water
flows across the surface of a polyhedral terrain in the direction of steepest
descent, and one where water only flows along the edges of a predefined
graph, for example a grid or a triangulation. In both cases each vertex has
an imprecise elevation, given by an interval of possible values, while its
(x, y)-coordinates are fixed. For the first model, we show that the problem
of deciding whether one vertex may be contained in the watershed of
another is NP-hard. In contrast, for the second model we give a simple
O(n log n) time algorithm to compute the minimal and the maximal
watershed of a vertex, where n is the number of edges of the graph.
On a grid model, we can compute the same in O(n) time.Peer ReviewedPostprint (published version
Fr\'echet Distance for Uncertain Curves
In this paper we study a wide range of variants for computing the (discrete
and continuous) Fr\'echet distance between uncertain curves. We define an
uncertain curve as a sequence of uncertainty regions, where each region is a
disk, a line segment, or a set of points. A realisation of a curve is a
polyline connecting one point from each region. Given an uncertain curve and a
second (certain or uncertain) curve, we seek to compute the lower and upper
bound Fr\'echet distance, which are the minimum and maximum Fr\'echet distance
for any realisations of the curves.
We prove that both the upper and lower bound problems are NP-hard for the
continuous Fr\'echet distance in several uncertainty models, and that the upper
bound problem remains hard for the discrete Fr\'echet distance. In contrast,
the lower bound (discrete and continuous) Fr\'echet distance can be computed in
polynomial time. Furthermore, we show that computing the expected discrete
Fr\'echet distance is #P-hard when the uncertainty regions are modelled as
point sets or line segments. The construction also extends to show #P-hardness
for computing the continuous Fr\'echet distance when regions are modelled as
point sets.
On the positive side, we argue that in any constant dimension there is a
FPTAS for the lower bound problem when is polynomially
bounded, where is the Fr\'echet distance and bounds the
diameter of the regions. We then argue there is a near-linear-time
3-approximation for the decision problem when the regions are convex and
roughly -separated. Finally, we also study the setting with
Sakoe--Chiba time bands, where we restrict the alignment between the two
curves, and give polynomial-time algorithms for upper bound and expected
discrete and continuous Fr\'echet distance for uncertainty regions modelled as
point sets.Comment: 48 pages, 11 figures. This is the full version of the paper to be
published in ICALP 202
Flow computations on imprecise terrains
We study water flow computation on imprecise terrains. We consider two approaches to modeling flow on a terrain: one where water flows across the surface of a polyhedral terrain in the direction of steepest descent, and one where water only flows along the edges of a predefined graph, for example a grid or a triangulation. In both cases each vertex has an imprecise elevation, given by an interval of possible values, while its (x,y)-coordinates are fixed. For the first model, we show that the problem of deciding whether one vertex may be contained in the watershed of another is NP-hard. In contrast, for the second model we give a simple O(n logn) time algorithm to compute the minimal and the maximal watershed of a vertex, where n is the number of edges of the graph. On a grid model, we can compute the same in O(n) time
Flow computations on imprecise terrains
We study water flow computation on imprecise terrains. We consider two approaches to modeling flow on a terrain: one where water
flows across the surface of a polyhedral terrain in the direction of steepest descent, and one where water only flows along
the edges of a prede ned graph, for example a grid or a triangulation. In both cases each
vertex has an imprecise elevation, given by an interval of possible values, while its (x; y)-coordinates are fi xed. For the fi rst model, we show that the problem of deciding whether one vertex may be contained in the watershed of another is NP-hard. In contrast, for the second model we give a simple O(n log n) time algorithm to compute the minimal and the maximal watershed of a vertex, or a set of vertices, where n is the number of edges of the graph. On a grid model, we can compute the same in O(n) time.Peer Reviewe
Flow computations on imprecise terrains
We study water flow computation on imprecise terrains. We consider two approaches to modeling flow on a terrain: one where water
flows across the surface of a polyhedral terrain in the direction of steepest descent, and one where water only flows along
the edges of a prede ned graph, for example a grid or a triangulation. In both cases each
vertex has an imprecise elevation, given by an interval of possible values, while its (x; y)-coordinates are fi xed. For the fi rst model, we show that the problem of deciding whether one vertex may be contained in the watershed of another is NP-hard. In contrast, for the second model we give a simple O(n log n) time algorithm to compute the minimal and the maximal watershed of a vertex, or a set of vertices, where n is the number of edges of the graph. On a grid model, we can compute the same in O(n) time.Peer Reviewe
Flow computations on imprecise terrains
We study water flow computation on imprecise terrains. We
consider two approaches to modeling flow on a terrain: one where water
flows across the surface of a polyhedral terrain in the direction of steepest
descent, and one where water only flows along the edges of a predefined
graph, for example a grid or a triangulation. In both cases each vertex has
an imprecise elevation, given by an interval of possible values, while its
(x, y)-coordinates are fixed. For the first model, we show that the problem
of deciding whether one vertex may be contained in the watershed of
another is NP-hard. In contrast, for the second model we give a simple
O(n log n) time algorithm to compute the minimal and the maximal
watershed of a vertex, where n is the number of edges of the graph.
On a grid model, we can compute the same in O(n) time.Peer Reviewe