Improved Deterministic Algorithms for Linear Programming in Low Dimensions
نویسنده
چکیده
At SODA’93, Chazelle and Matoušek presented a derandomization of Clarkson’s sampling-based algorithm [FOCS’88] for solving linear programs with n constraints and d variables in dn deterministic time. The time bound can be improved to dn with subsequent work by Brönnimann, Chazelle, and Matoušek [FOCS’93]. We first point out a much simpler derandomization of Clarkson’s algorithm that avoids ε-approximations and runs in dn time. We then describe a few additional ideas that eventually improve the deterministic time bound to dn.
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