6,729 research outputs found
On the Construction of Polar Codes
We consider the problem of efficiently constructing polar codes over binary
memoryless symmetric (BMS) channels. The complexity of designing polar codes
via an exact evaluation of the polarized channels to find which ones are "good"
appears to be exponential in the block length. In \cite{TV11}, Tal and Vardy
show that if instead the evaluation if performed approximately, the
construction has only linear complexity. In this paper, we follow this approach
and present a framework where the algorithms of \cite{TV11} and new related
algorithms can be analyzed for complexity and accuracy. We provide numerical
and analytical results on the efficiency of such algorithms, in particular we
show that one can find all the "good" channels (except a vanishing fraction)
with almost linear complexity in block-length (except a polylogarithmic
factor).Comment: In ISIT 201
Query Learning with Exponential Query Costs
In query learning, the goal is to identify an unknown object while minimizing
the number of "yes" or "no" questions (queries) posed about that object. A
well-studied algorithm for query learning is known as generalized binary search
(GBS). We show that GBS is a greedy algorithm to optimize the expected number
of queries needed to identify the unknown object. We also generalize GBS in two
ways. First, we consider the case where the cost of querying grows
exponentially in the number of queries and the goal is to minimize the expected
exponential cost. Then, we consider the case where the objects are partitioned
into groups, and the objective is to identify only the group to which the
object belongs. We derive algorithms to address these issues in a common,
information-theoretic framework. In particular, we present an exact formula for
the objective function in each case involving Shannon or Renyi entropy, and
develop a greedy algorithm for minimizing it. Our algorithms are demonstrated
on two applications of query learning, active learning and emergency response.Comment: 15 page
Exploring Photometric Redshifts as an Optimization Problem: An Ensemble MCMC and Simulated Annealing-Driven Template-Fitting Approach
Using a grid of million elements () adapted from
COSMOS photometric redshift (photo-z) searches, we investigate the general
properties of template-based photo-z likelihood surfaces. We find these
surfaces are filled with numerous local minima and large degeneracies that
generally confound rapid but "greedy" optimization schemes, even with
additional stochastic sampling methods. In order to robustly and efficiently
explore these surfaces, we develop BAD-Z [Brisk Annealing-Driven Redshifts
(Z)], which combines ensemble Markov Chain Monte Carlo (MCMC) sampling with
simulated annealing to sample arbitrarily large, pre-generated grids in
approximately constant time. Using a mock catalog of 384,662 objects, we show
BAD-Z samples times more efficiently compared to a brute-force
counterpart while maintaining similar levels of accuracy. Our results represent
first steps toward designing template-fitting photo-z approaches limited mainly
by memory constraints rather than computation time.Comment: 14 pages, 8 figures; submitted to MNRAS; comments welcom
- …