Abstract:
The complexity of a nondeterministic function is the minimum possible complexity of its determinisation. The entropy of a nondeterministic function, F, is minus the logarithm of the ratio between the number of determinisations of F and the number of all deterministic functions.
We obtain an upper bound on the complexity of a nondeterministic function with restricted entropy for the worst case.
These bounds have strong applications in the problem of algorithm derandomization. A lot of randomized algorithms can be converted to deterministic ones if we have an effective hitting set with certain parameters (a set is hitting for a set system if it has a nonempty intersection with any set from the system).
Linial,
Luby, Saks and Zuckerman (1993) constructed the best effective hitting set
for the system of k-value, n-dimensional rectangles. The set size is
polynomial in 
.
Our bounds of nondeterministic
functions complexity offer a possibility to construct an effective hitting
set for this system with almost linear size in .
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