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COMPGEOM
1998
ACM

Geometric Applications of a Randomized Optimization Technique

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Geometric Applications of a Randomized Optimization Technique
Abstract. We propose a simple, general, randomized technique to reduce certain geometric optimization problems to their corresponding decision problems. These reductions increase the expected time complexity by only a constant factor and eliminate extra logarithmic factors in previous, often more complicated, deterministic approaches (such as parametric searching). Faster algorithms are thus obtained for a variety of problems in computational geometry: finding minimal k-point subsets, matching point sets under translation, computing rectilinear p-centers and discrete 1-centers, and solving linear programs with k violations.
Timothy M. Chan
Added 05 Aug 2010
Updated 05 Aug 2010
Type Conference
Year 1998
Where COMPGEOM
Authors Timothy M. Chan
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