E
E. Alper Yıldırım
Researcher at University of Edinburgh
Publications - 34
Citations - 1216
E. Alper Yıldırım is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Quadratic programming & Linear programming. The author has an hindex of 13, co-authored 32 publications receiving 1085 citations. Previous affiliations of E. Alper Yıldırım include Bilkent University & Türkiye İş Bankası.
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On Khachiyan's algorithm for the computation of minimum-volume enclosing ellipsoids
TL;DR: The algorithm is a modification of the algorithm of Kumar and Yildirim, which combines Khachiyan's BCD method with a simple initialization scheme to achieve a slightly improved polynomial complexity result, and which returns a small ''core set.
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Approximate minimum enclosing balls in high dimensions using core-sets
TL;DR: This work develops (1 + ε)-approximation algorithms that perform well in practice, especially for very high dimensions, in addition to having provable guarantees, and proves the existence of core-sets of size O(1/ε), improving the previous bound of O( 1/ε2).
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Warm-Start Strategies in Interior-Point Methods for Linear Programming
TL;DR: Worst-case estimates of the number of iterations required to converge to a solution of the perturbed instance from the warm-start points are obtained, showing that these estimates depend on the size ofThe perturbation and on the conditioning and other properties of the problem instances.
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Two Algorithms for the Minimum Enclosing Ball Problem
TL;DR: The second algorithm asymptotically exhibits linear convergence, indicating that the latter algorithm indeed terminates faster with smaller core sets in comparison with the first one, and establishes the existence of a core set of size $O(1/\epsilon)$ for a much wider class of input sets.
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Implementation of warm-start strategies in interior-point methods for linear programming in fixed dimension
Elizabeth John,E. Alper Yıldırım +1 more
TL;DR: The experiments reveal that each of the warm-start strategies leads to a reduction in the number of interior-point iterations especially for smaller perturbation and for perturbations of fewer data components in comparison with cold start.