349 research outputs found
List decoding group homomorphisms between supersolvable groups
We show that the set of homomorphisms between two supersolvable groups can be
locally list decoded up to the minimum distance of the code, extending the
results of Dinur et al who studied the case where the groups are abelian.
Moreover, when specialized to the abelian case, our proof is more streamlined
and gives a better constant in the exponent of the list size. The constant is
improved from about 3.5 million to 105.Comment: 11 page
Delays and the Capacity of Continuous-time Channels
Any physical channel of communication offers two potential reasons why its
capacity (the number of bits it can transmit in a unit of time) might be
unbounded: (1) Infinitely many choices of signal strength at any given instant
of time, and (2) Infinitely many instances of time at which signals may be
sent. However channel noise cancels out the potential unboundedness of the
first aspect, leaving typical channels with only a finite capacity per instant
of time. The latter source of infinity seems less studied. A potential source
of unreliability that might restrict the capacity also from the second aspect
is delay: Signals transmitted by the sender at a given point of time may not be
received with a predictable delay at the receiving end. Here we examine this
source of uncertainty by considering a simple discrete model of delay errors.
In our model the communicating parties get to subdivide time as microscopically
finely as they wish, but still have to cope with communication delays that are
macroscopic and variable. The continuous process becomes the limit of our
process as the time subdivision becomes infinitesimal. We taxonomize this class
of communication channels based on whether the delays and noise are stochastic
or adversarial; and based on how much information each aspect has about the
other when introducing its errors. We analyze the limits of such channels and
reach somewhat surprising conclusions: The capacity of a physical channel is
finitely bounded only if at least one of the two sources of error (signal noise
or delay noise) is adversarial. In particular the capacity is finitely bounded
only if the delay is adversarial, or the noise is adversarial and acts with
knowledge of the stochastic delay. If both error sources are stochastic, or if
the noise is adversarial and independent of the stochastic delay, then the
capacity of the associated physical channel is infinite
Patterns hidden from simple algorithms
Is the number 9021960864034418159813 random? Educated opinions might
vary from “No! No single string can be random,” to the more contemptuous
”Come on! Those are just the 714th to 733rd digits of π.” Yet, to my limited
mind, the string did appear random. Is there a way to use some formal
mathematics to justify my naïveté? The modern theory of pseudorandomness indeed manages to explain such phenomena, where strings appear random to simple minds. The key, this theory argues, is that randomness is really in the “eyes of the beholder,” or rather in the computing power of the tester of randomness. More things appear random to simpler, or resource limited, algorithms than to complex, powerful, algorithms
Communication Complexity of Permutation-Invariant Functions
Motivated by the quest for a broader understanding of communication
complexity of simple functions, we introduce the class of
"permutation-invariant" functions. A partial function is permutation-invariant if for every bijection
and every , it is the case that . Most of the commonly studied functions
in communication complexity are permutation-invariant. For such functions, we
present a simple complexity measure (computable in time polynomial in given
an implicit description of ) that describes their communication complexity
up to polynomial factors and up to an additive error that is logarithmic in the
input size. This gives a coarse taxonomy of the communication complexity of
simple functions. Our work highlights the role of the well-known lower bounds
of functions such as 'Set-Disjointness' and 'Indexing', while complementing
them with the relatively lesser-known upper bounds for 'Gap-Inner-Product'
(from the sketching literature) and 'Sparse-Gap-Inner-Product' (from the recent
work of Canonne et al. [ITCS 2015]). We also present consequences to the study
of communication complexity with imperfectly shared randomness where we show
that for total permutation-invariant functions, imperfectly shared randomness
results in only a polynomial blow-up in communication complexity after an
additive overhead
Approximate Graph Coloring by Semidefinite Programming
We consider the problem of coloring k-colorable graphs with the fewest
possible colors. We present a randomized polynomial time algorithm that colors
a 3-colorable graph on vertices with min O(Delta^{1/3} log^{1/2} Delta log
n), O(n^{1/4} log^{1/2} n) colors where Delta is the maximum degree of any
vertex. Besides giving the best known approximation ratio in terms of n, this
marks the first non-trivial approximation result as a function of the maximum
degree Delta. This result can be generalized to k-colorable graphs to obtain a
coloring using min O(Delta^{1-2/k} log^{1/2} Delta log n), O(n^{1-3/(k+1)}
log^{1/2} n) colors. Our results are inspired by the recent work of Goemans and
Williamson who used an algorithm for semidefinite optimization problems, which
generalize linear programs, to obtain improved approximations for the MAX CUT
and MAX 2-SAT problems. An intriguing outcome of our work is a duality
relationship established between the value of the optimum solution to our
semidefinite program and the Lovasz theta-function. We show lower bounds on the
gap between the optimum solution of our semidefinite program and the actual
chromatic number; by duality this also demonstrates interesting new facts about
the theta-function
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