ACM's Special Interest Group on Algorithms and Computation Theory named the University of Southern California Viterbi School of Engineering Seely G. Mudd Professor of Computer Science and Mathematics Shang-Hua Teng, and his collaborator, Yale University professor of applied mathematics and computer science Daniel A. Spielman, recipients of the Symposium on Theory of Computing Test of Time Award.
Teng and Spielman authored a paper on smoothed analysis of algorithms, which offers a more realistic comprehension of algorithmic performance.
The authors determined that algorithms, especially the simplex algorithm for linear programming, function as long as the input has noise, because real-world data typically contains noise.
Said Teng, "Our theory demonstrates that these properties can in fact be helpful to algorithms in practice, because under these conditions, the worst-case scenarios are harder to arise."
These findings have been applied in a wide range of practical algorithms, including faster Internet communications, deep learning, data mining, game theory, and personalized recommendation systems.
From USC Viterbi News
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