Titlebook: Randomization and Approximation Techniques in Computer Science; Second International Michael Luby,José D. P. Rolim,Maria Serna Conference p

優(yōu)雅

柳樹；枯黃

滑動

Obstruction

敬禮

Arboreal

Talagrand’s Inequality and Locality in Distributed Computing analysis of distributed randomized algorithms that work in the locality paradigm. Two features of the inequality are crucially used in the analysis: first, very refined control on the influence of the underlying variables can be exercised to get signicantly stronger bounds by exploiting the non-uni

考古學

微塵

Combinatorial Linear Programming: Geometry Can Helppolynomial on all actual linear programs in the class. In contrast, the subexponential analysis is known to be best possible for general instances in . Thus, we identify a “geometric” property of linear programming that goes beyond all abstract notions previously employed in generalized linear progr

Catheter

A Note on Bounding the Mixing Time by Linear ProgrammingThe linear minimization program we construct has one variable per state and (the square of) its solution is an upper bound on the mixing time. The proof of this theorem uses the coupling technique and a generalization of the distance function commonly used in this context. Explicit solutions are obt

忘川河