We address the following problem: how to execute any algorithm P, for an unbounded number of executions, in the presence of an adversary who observes partial information on the internal state of the computation during executions. The security guarantee is that the adversary learns nothing, beyond P's input/output behavior.
This ... more >>>
We survey research that studies the connection between the computational complexity
of optimization problems on the one hand, and the duality gap between the primal and
dual optimization problems on the other. To our knowledge, this is the first survey that
connects the two very important areas. We further look ...
more >>>
We develop a new method for proving explicit approximation lower bounds for TSP problems with bounded metrics improving on the best up to now known bounds. They almost match the best known bounds for unbounded metric TSP problems. In particular, we prove the best known lower bound for TSP with ... more >>>