Smoothed analysis combines elements over worst-case and average case analysis. For an instance x, the smoothed complexity is the average complexity of an instance obtained from x by a perturbation. The smoothed complexity of a problem is the worst smoothed complexity of any instance. Spielman and Teng introduced this notion for continuous problems. We apply the concept to combinatorial problems and study the smoothed complexity of three classical discrete problems: quicksort, left-to-right maxima counting, and shortest paths
Previous lectures on smoothed analysis sought a better theoretical understanding of the empirical pe...
A left-to-right maximum in a sequence of n numbers $s_1, ..., s_n$ is a number that is strictly larg...
Spielman’s smoothed complexity - a hybrid between worst and average case complexity measures - relie...
Smoothed analysis combines elements over worst-case and average case analysis. For an instance $x$, ...
Smoothed analysis combines elements over worst-case and average case analysis. For an instance $x$, ...
Smoothed analysis combines elements over worst-case and average case analysis. For an instance x the...
Smoothed analysis combines elements over worst-case and average case analysis. For an instance $x$, ...
Smoothed analysis is a new way of analyzing algorithms introduced by Spielman and Teng. Classical me...
Abstract. We introduce the smoothed analysis of algorithms, which continuously interpolates between ...
Many algorithms perform very well in practice, but have a poor worst-case performance. The reason fo...
The minimum-cost flow problem is a classic problem in combinatorial optimization with various applic...
Spielman’s smoothed complexity - a hybrid between worst and average case complexity measures - relie...
The minimum-cost flow problem is a classic problem in combinatorial optimization with various applic...
The minimum-cost flow problem is a classic problem in combinatorial optimization with various applic...
A left-to-right maximum in a sequence of n numbers s(1), ..., s(n) is a number that is strictly larg...
Previous lectures on smoothed analysis sought a better theoretical understanding of the empirical pe...
A left-to-right maximum in a sequence of n numbers $s_1, ..., s_n$ is a number that is strictly larg...
Spielman’s smoothed complexity - a hybrid between worst and average case complexity measures - relie...
Smoothed analysis combines elements over worst-case and average case analysis. For an instance $x$, ...
Smoothed analysis combines elements over worst-case and average case analysis. For an instance $x$, ...
Smoothed analysis combines elements over worst-case and average case analysis. For an instance x the...
Smoothed analysis combines elements over worst-case and average case analysis. For an instance $x$, ...
Smoothed analysis is a new way of analyzing algorithms introduced by Spielman and Teng. Classical me...
Abstract. We introduce the smoothed analysis of algorithms, which continuously interpolates between ...
Many algorithms perform very well in practice, but have a poor worst-case performance. The reason fo...
The minimum-cost flow problem is a classic problem in combinatorial optimization with various applic...
Spielman’s smoothed complexity - a hybrid between worst and average case complexity measures - relie...
The minimum-cost flow problem is a classic problem in combinatorial optimization with various applic...
The minimum-cost flow problem is a classic problem in combinatorial optimization with various applic...
A left-to-right maximum in a sequence of n numbers s(1), ..., s(n) is a number that is strictly larg...
Previous lectures on smoothed analysis sought a better theoretical understanding of the empirical pe...
A left-to-right maximum in a sequence of n numbers $s_1, ..., s_n$ is a number that is strictly larg...
Spielman’s smoothed complexity - a hybrid between worst and average case complexity measures - relie...
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