Ahlswede and Winter introduced a Chernoff bound for matrix-valued random variables, which is a non-trivial generalization of the usual Chernoff bound for real-valued random variables. We present an efficient derandomization of their bound using the method of pessimistic estimators (see Raghavan). As a consequence, we derandomize a construction of Alon ... more >>>

We give a simple proof for the sample complexity bound $O~(1/\epsilon^4)$ of absolute approximation of MAX-CUT. The proof depends on a new analysis method for linear programs (LPs) underlying MAX-CUT which could be also of independent interest.

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Combinatorial property testing deals with the following relaxation
of decision problems: Given a fixed property and an input $x$, one
wants to decide whether $x$ satisfies the property or is ``far''
from satisfying it. The main focus of property testing is in
identifying large families of properties that can be ... more >>>