13,101 research outputs found
Faster Geometric Algorithms via Dynamic Determinant Computation
The computation of determinants or their signs is the core procedure in many
important geometric algorithms, such as convex hull, volume and point location.
As the dimension of the computation space grows, a higher percentage of the
total computation time is consumed by these computations. In this paper we
study the sequences of determinants that appear in geometric algorithms. The
computation of a single determinant is accelerated by using the information
from the previous computations in that sequence.
We propose two dynamic determinant algorithms with quadratic arithmetic
complexity when employed in convex hull and volume computations, and with
linear arithmetic complexity when used in point location problems. We implement
the proposed algorithms and perform an extensive experimental analysis. On one
hand, our analysis serves as a performance study of state-of-the-art
determinant algorithms and implementations. On the other hand, we demonstrate
the supremacy of our methods over state-of-the-art implementations of
determinant and geometric algorithms. Our experimental results include a 20 and
78 times speed-up in volume and point location computations in dimension 6 and
11 respectively.Comment: 29 pages, 8 figures, 3 table
Largest Empty Circle Centered on a Query Line
The Largest Empty Circle problem seeks the largest circle centered within the
convex hull of a set of points in and devoid of points
from . In this paper, we introduce a query version of this well-studied
problem. In our query version, we are required to preprocess so that when
given a query line , we can quickly compute the largest empty circle
centered at some point on and within the convex hull of .
We present solutions for two special cases and the general case; all our
queries run in time. We restrict the query line to be horizontal in
the first special case, which we preprocess in time and
space, where is the slow growing inverse of the Ackermann's
function. When the query line is restricted to pass through a fixed point, the
second special case, our preprocessing takes time and space. We use insights from the two special cases to solve the
general version of the problem with preprocessing time and space in and respectively.Comment: 18 pages, 13 figure
Convex Tours of Bounded Curvature
We consider the motion planning problem for a point constrained to move along
a smooth closed convex path of bounded curvature. The workspace of the moving
point is bounded by a convex polygon with m vertices, containing an obstacle in
a form of a simple polygon with vertices. We present an O(m+n) time
algorithm finding the path, going around the obstacle, whose curvature is the
smallest possible.Comment: 11 pages, 5 figures, abstract presented at European Symposium on
Algorithms 199
The Traveling Salesman Problem in the Natural Environment
Is it possible for humans to navigate in the natural environment wherein the path taken between various destinations is 'optimal' in some way? In the domain of optimization this challenge is traditionally framed as the "Traveling Salesman Problem" (TSP). What strategies and ecological considerations are plausible for human navigation? When given a two-dimensional map-like presentation of the destinations, participants solve this optimization exceptionally well (only 2-3% longer than optimum)^1, 2^. In the following experiments we investigate the effect of effort and its environmental affordance on navigation decisions when humans solve the TSP in the natural environment. Fifteen locations were marked on two outdoor landscapes with flat and varied terrains respectively. Performance in the flat-field condition was excellent (∼6% error) and was worse but still quite good in the variable-terrain condition (∼20% error), suggesting participants do not globally pre-plan routes but rather develop them on the fly. We suggest that perceived effort guides participant solutions due to the dynamic constraints of effortful locomotion and obstacle avoidance
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