D. Spielman and S. Teng. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. In Proc. 33rd ACM STOC, pages 296--305, 2001. Home/Search Document Not in Database Summary Related Articles Check |
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Distributed Algorithmic Mechanism Design - Sami (2003) (1 citation) (Correct)
....Instead, we insist that any hardness result hold over an open set of cost or preference values; this means that the hardness holds over a region of preference space with non zero volume, instead of at isolated points. This is similar in spirit to the smoothed analysis of Spielman and Teng [ST01] First, we observe that the LCP mechanism satisfies these properties, provided the costs are similar to each other, not very skewed. We follow the notation in Section 4.6.3, using d to denote the length (in hops) of the longest chosen route and d # to denote the length of the longest relevant ....
Daniel Spielman and Shang-Hua Teng. Smoothed analysis of algorithms: why the simplex algorithm usually takes polynomial time. In Proceedings of the 33rd Annual ACM Symposium on Theory of Computing (STOC '01), pages 296--305. ACM Press, New York, July 2001.
Smoothed Analysis of Three Combinatorial Problems - Banderier, Mehlhorn, Beier (2003) (3 citations) (Correct)
....and analysis nicely meets computer science to give incredibly accurate informations, for example leading to full asymptotic expansions for the complexity of some algorithms. In this article, we concentrate on a new notion, called smoothed analysis (recently introduced by Spielman and Teng [20]) which is intermediate between average case analysis and worst case analysis and which (we will see) allows to follow the nice wedding initiated by Knuth. The smoothed complexity of an algorithm is max E y2U (x) C(y) where x ranges over all inputs, y is a random instance in a ....
....of an instance is simply an interval around the instance. In the situation on the left, the smoothed complexity will be equal to the worst case complexity (for all small enough ) and in the situation on the right, the smoothed complexity decreases sharply as a function of . Spielman and Teng [20] showed that the smoothed complexity of the simplex algorithm (with the shadow vertex pivot rule) for linear programming is polynomial. Linear programming is a continuous problem. The input is a sequence of real numbers (a cost vector, a constraint matrix, a right hand side) The smoothing ....
Daniel A. Spielman and Shang-Hua Teng. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. In proceedings of the The Thirty-Third Annual ACM Symposium on Theory of Computing, pages 296-3051, 2001.
Randomization Constrained - Liberatore (2002) (Correct)
....of the player is subject to additional constraints. The extension is applicable to several contexts in the area of randomized algorithms, including multi objective optimization problems [20] performance tail of randomized algorithms [14] the resource augmentation method [11] smoothed analysis [23], and loose competitiveness [27] The rest of this section discusses the consequences of our main duality result for algorithm design and analysis. Algorithms. Several optimization problem naturally lend themselves to a multi objective formulation, where algorithms can attempt to minimize any one ....
....comparison is expressed by the penalty E[d] which is embedded in the adversary cost increase model. The same argument holds mostly unchanged for smoothed analysis, where the algorithm cost is reduced by a d(x) factor corresponding to its expected cost in a neighborhood of the original instance x [23]. Thus, the two techniques of RAM and smoothed analysis are given a unified interpretation in terms of the adversary cost increase model. In particular, results on randomized algorithms in either of the two models can be translated into results on deterministic algorithms against an upper bounded ....
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Daniel A. Spielman and Shang-Hua Teng. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. In Proceedings of the Thirty-third Annual ACM Symposium on the Theory of Computing, 2001.
Structure and Hierarchy in Real-Time Systems - Möller (2002) (Correct)
....most e#cient algorithm for solving linear programs, though it has a worst case exponential lower bound and polynomial algorithms are known. A recent paper explains this paradox by redefining the measure of computational complexity according to assignments that are almost solutions of the problem [ST01] According to this measure, which is a hybrid between worst case and average case analysis, the shadow vertex simplex algorithm is polynomial. In formal verification, an algorithm with high inherent complexity is not necessarily useless. The Mona tool [BK95,HJJ 97,KM01] demonstrates that an ....
Daniel A. Spielman and Shang-Hua Teng. Smoothed Analysis of Algorithms: Why the Simplex Algorithm Usually Takes Polynomial Time. In Proceedings of the 33rd Annual ACM Symposium on the Theory of Computing, pages 296--305, 2001. 7
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Daniel Spielman and Shang-Hua Teng. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. In Proceedings of the 33rd Annual ACM Symposium on the Theory of Computing (STOC '01), pages 296-305, 2001. Available at http://math.mit.edu/spielman/SmoothedAnalysis/.
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D. Spielman and S. Teng. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. In Proc. 33rd ACM STOC, pages 296--305, 2001.
Smoothed Analysis of Three Combinatorial Problems - Banderier, Beier, Mehlhorn (2003) (3 citations) (Correct)
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Daniel A. Spielman and Shang-Hua Teng. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. In Proceedings of the ThirtyThird Annual ACM Symposium on Theory of Computing, pages 296--3051, 2001.
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D. A. Spielman and S.-H. Teng. Smoothed Analysis of Algorithms: Why The Simplex Algorithm Usually Takes Polynomial Time. In Journal of the ACM, volume 51(3), pages 385--463, 2004.
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Daniel A. Spielman and Shang-Hua Teng. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time, In Journal of the ACM 51(3), 385-- 463, 2004
Algorithm Engineering - Demetrescu, Finocchi, Italiano (2003) (Correct)
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D.A. Spielman and S.H. Teng. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. In Proc. 33rd Annual ACM Symposium on Theory of Computing (STOC 01), pages 296-305, 2001.
Randomized Search Heuristics as an Alternative to Exact.. - Wegener (2004) (Correct)
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Spielman, D. A. and Teng, S.-H. (2001). Smoothed analysis of algorithms: why the simplex algorithm usually takes polynomial time. Proc. of 33rd ACM Symp. on Theory of Computing (STOC), 296--305.
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SPIELMAN, D., AND TENG, S. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. In Proceedings of the 33rd ACM Symposium on Theory of Computing (STOC) (2001), pp. 296--305.
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D. Spielman and S.H. Teng. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. In Proceedings of the 33rd Annual ACM Symposium on Theory of Computing, pages 296-305, 2001.
Algorithm Engineering - Demetrescu, Finocchi, Italiano (2003) (Correct)
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D.A. Spielman and S.H. Teng. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. In Proc. 33rd Annual ACM Symposium on Theory of Computing (STOC 01), pages 296--305, 2001.
Topology Matters: Smoothed Competitiveness of Metrical Task.. - Guido Schafer And (2003) (Correct)
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D. Spielman and S. H. Teng. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. In ACM Symposium on Theory of Computing, 2001.
Randomized Search Heuristics as an Alternative to Exact.. - Wegener (2004) (Correct)
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Spielman, D. A. and Teng, S.-H. (2001). Smoothed analysis of algorithms: why the simplex algorithm usually takes polynomial time. Proc. of 33rd ACM Symp. on Theory of Computing (STOC), 296--305.
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D. Spielman and S.-H. Teng. Smoothed analysis of algorithms: why the simplex algorithm usually takes polynomial time. In Proceedings of the 33rd ACM Symposium on Theory of Computing (STOC '01), pages 296--305. ACM Press, New York, July 2001.
Smoothed Motion Complexity - Damerow, der Heide, Räcke.. (Correct)
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SPIELMAN, D., AND TENG, S. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. In Proceedings of the 33rd ACM Symposium on Theory of Computing (STOC) (2001), pp. 296--305.
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D. Spielman and S.H. Teng, Smoothed Analysis of Algorithms: Why The Simplex Algorithm Usually Takes Polynomial Time, Proceedings of the The Thirty-Third Annual ACM Symposium on Theory of Computing, (2001) 296-305.
Algorithm Engineering - Camil Demetrescu Irene (2003) (Correct)
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D.A. Spielman and S.H. Teng. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. In Proc. 33rd Annual ACM Symposium on Theory of Computing (STOC 01), pages 296-305, 2001.
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D. A. Spielman and Shang-Hua Teng. Smoothed Analysis of Algorithms: Why The Simplex Algorithm Usually Takes Polynomial Time. Proceedings of the 33rd ACM Symposium on Theory of Computing (STOC), 296--305, 2001.
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