On Linear-Time Deterministic Algorithms for Optimization Problems in Fixed Dimensions
نویسندگان
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, e.g., computing the minimum-volume ellipsoid enclosing a set of n points, nding the maximum volume ellipsoid in the intersection of n halfspaces.
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