4,019 research outputs found
Sum of squares lower bounds for refuting any CSP
Let be a nontrivial -ary predicate. Consider a
random instance of the constraint satisfaction problem on
variables with constraints, each being applied to randomly
chosen literals. Provided the constraint density satisfies , such
an instance is unsatisfiable with high probability. The \emph{refutation}
problem is to efficiently find a proof of unsatisfiability.
We show that whenever the predicate supports a -\emph{wise uniform}
probability distribution on its satisfying assignments, the sum of squares
(SOS) algorithm of degree
(which runs in time ) \emph{cannot} refute a random instance of
. In particular, the polynomial-time SOS algorithm requires
constraints to refute random instances of
CSP when supports a -wise uniform distribution on its satisfying
assignments. Together with recent work of Lee et al. [LRS15], our result also
implies that \emph{any} polynomial-size semidefinite programming relaxation for
refutation requires at least constraints.
Our results (which also extend with no change to CSPs over larger alphabets)
subsume all previously known lower bounds for semialgebraic refutation of
random CSPs. For every constraint predicate~, they give a three-way hardness
tradeoff between the density of constraints, the SOS degree (hence running
time), and the strength of the refutation. By recent algorithmic results of
Allen et al. [AOW15] and Raghavendra et al. [RRS16], this full three-way
tradeoff is \emph{tight}, up to lower-order factors.Comment: 39 pages, 1 figur
Random subgraphs make identification affordable
An identifying code of a graph is a dominating set which uniquely determines
all the vertices by their neighborhood within the code. Whereas graphs with
large minimum degree have small domination number, this is not the case for the
identifying code number (the size of a smallest identifying code), which indeed
is not even a monotone parameter with respect to graph inclusion.
We show that every graph with vertices, maximum degree
and minimum degree , for some
constant , contains a large spanning subgraph which admits an identifying
code with size . In particular, if
, then has a dense spanning subgraph with identifying
code , namely, of asymptotically optimal size. The
subgraph we build is created using a probabilistic approach, and we use an
interplay of various random methods to analyze it. Moreover we show that the
result is essentially best possible, both in terms of the number of deleted
edges and the size of the identifying code
On Closeness to k-Wise Uniformity
A probability distribution over {-1, 1}^n is (epsilon, k)-wise uniform if, roughly, it is epsilon-close to the uniform distribution when restricted to any k coordinates. We consider the problem of how far an (epsilon, k)-wise uniform distribution can be from any globally k-wise uniform distribution. We show that every (epsilon, k)-wise uniform distribution is O(n^{k/2}epsilon)-close to a k-wise uniform distribution in total variation distance. In addition, we show that this bound is optimal for all even k: we find an (epsilon, k)-wise uniform distribution that is Omega(n^{k/2}epsilon)-far from any k-wise uniform distribution in total variation distance. For k=1, we get a better upper bound of O(epsilon), which is also optimal.
One application of our closeness result is to the sample complexity of testing whether a distribution is k-wise uniform or delta-far from k-wise uniform. We give an upper bound of O(n^{k}/delta^2) (or O(log n/delta^2) when k = 1) on the required samples. We show an improved upper bound of O~(n^{k/2}/delta^2) for the special case of testing fully uniform vs. delta-far from k-wise uniform. Finally, we complement this with a matching lower bound of Omega(n/delta^2) when k = 2.
Our results improve upon the best known bounds from [Alon et al., 2007], and have simpler proofs
Optical Quantum Random Number Generator
A physical random number generator based on the intrinsic randomness of
quantum mechanics is described. The random events are realized by the choice of
single photons between the two outputs of a beamsplitter. We present a simple
device, which minimizes the impact of the photon counters' noise, dead-time and
after pulses.Comment: 3 pages + 1 figur
- …