On linear-time deterministic algorithms for optimization problems in xed dimension
نویسنده
چکیده
We show that with recently developed derandomization techniques, one can convert Clarkson's randomized algorithm for linear programming in xed dimension into a lineartime deterministic one. The constant of proportionality is d, which is better than for previously known such algorithms. We show that the algorithm works in a fairly general abstract setting, which allows us to solve various other problems (such as nding the maximum volume ellipsoid inscribed into the intersection of n halfspaces) in linear time.
منابع مشابه
On Linear-Time Deterministic Algorithms for Optimization Problems in Fixed Dimensions
We show that with recently developed derandomization techniques, one can convert Clarkson's randomized algorithm for linear programming in xed dimension into a lineartime deterministic one. The constant of proportionality is d, which is better than for previously known such algorithms. We show that the algorithm works in a fairly general abstract setting, which allows us to solve various other ...
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