Abstract
We show that with recently developed derandomization techniques, one can convert Clarkson's randomized algorithm for linear programming in fixed dimension into a linear-time deterministic algorithm. The constant of proportionality is d O(d) , which is better than those for previously known 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 and finding the maximum volume ellipsoid in the intersection of n halfspaces.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 579-597 |
| Number of pages | 19 |
| Journal | Journal of Algorithms |
| Volume | 21 |
| Issue number | 3 |
| DOIs | |
| State | Published - Jan 1 1996 |
All Science Journal Classification (ASJC) codes
- Control and Optimization
- Computational Mathematics
- Computational Theory and Mathematics
Fingerprint Dive into the research topics of 'On Linear-Time Deterministic Algorithms for Optimization Problems in Fixed Dimension'. Together they form a unique fingerprint.
Cite this
Chazelle, B., & Matoušek, J. (1996). On Linear-Time Deterministic Algorithms for Optimization Problems in Fixed Dimension. Journal of Algorithms, 21(3), 579-597. https://doi.org/10.1006/jagm.1996.0060