31,712 research outputs found
Approximately Sampling Elements with Fixed Rank in Graded Posets
Graded posets frequently arise throughout combinatorics, where it is natural
to try to count the number of elements of a fixed rank. These counting problems
are often -complete, so we consider approximation algorithms for
counting and uniform sampling. We show that for certain classes of posets,
biased Markov chains that walk along edges of their Hasse diagrams allow us to
approximately generate samples with any fixed rank in expected polynomial time.
Our arguments do not rely on the typical proofs of log-concavity, which are
used to construct a stationary distribution with a specific mode in order to
give a lower bound on the probability of outputting an element of the desired
rank. Instead, we infer this directly from bounds on the mixing time of the
chains through a method we call .
A noteworthy application of our method is sampling restricted classes of
integer partitions of . We give the first provably efficient Markov chain
algorithm to uniformly sample integer partitions of from general restricted
classes. Several observations allow us to improve the efficiency of this chain
to require space, and for unrestricted integer partitions,
expected time. Related applications include sampling permutations
with a fixed number of inversions and lozenge tilings on the triangular lattice
with a fixed average height.Comment: 23 pages, 12 figure
Segmented compressed sampling for analog-to-information conversion: Method and performance analysis
A new segmented compressed sampling method for analog-to-information
conversion (AIC) is proposed. An analog signal measured by a number of parallel
branches of mixers and integrators (BMIs), each characterized by a specific
random sampling waveform, is first segmented in time into segments. Then
the sub-samples collected on different segments and different BMIs are reused
so that a larger number of samples than the number of BMIs is collected. This
technique is shown to be equivalent to extending the measurement matrix, which
consists of the BMI sampling waveforms, by adding new rows without actually
increasing the number of BMIs. We prove that the extended measurement matrix
satisfies the restricted isometry property with overwhelming probability if the
original measurement matrix of BMI sampling waveforms satisfies it. We also
show that the signal recovery performance can be improved significantly if our
segmented AIC is used for sampling instead of the conventional AIC. Simulation
results verify the effectiveness of the proposed segmented compressed sampling
method and the validity of our theoretical studies.Comment: 32 pages, 5 figures, submitted to the IEEE Transactions on Signal
Processing in April 201
Efficient generation of random derangements with the expected distribution of cycle lengths
We show how to generate random derangements efficiently by two different
techniques: random restricted transpositions and sequential importance
sampling. The algorithm employing restricted transpositions can also be used to
generate random fixed-point-free involutions only, a.k.a. random perfect
matchings on the complete graph. Our data indicate that the algorithms generate
random samples with the expected distribution of cycle lengths, which we
derive, and for relatively small samples, which can actually be very large in
absolute numbers, we argue that they generate samples indistinguishable from
the uniform distribution. Both algorithms are simple to understand and
implement and possess a performance comparable to or better than those of
currently known methods. Simulations suggest that the mixing time of the
algorithm based on random restricted transpositions (in the total variance
distance with respect to the distribution of cycle lengths) is
with and the length of the
derangement. We prove that the sequential importance sampling algorithm
generates random derangements in time with probability of
failing.Comment: This version corrected and updated; 14 pages, 2 algorithms, 2 tables,
4 figure
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
Many matching, tracking, sorting, and ranking problems require probabilistic
reasoning about possible permutations, a set that grows factorially with
dimension. Combinatorial optimization algorithms may enable efficient point
estimation, but fully Bayesian inference poses a severe challenge in this
high-dimensional, discrete space. To surmount this challenge, we start with the
usual step of relaxing a discrete set (here, of permutation matrices) to its
convex hull, which here is the Birkhoff polytope: the set of all
doubly-stochastic matrices. We then introduce two novel transformations: first,
an invertible and differentiable stick-breaking procedure that maps
unconstrained space to the Birkhoff polytope; second, a map that rounds points
toward the vertices of the polytope. Both transformations include a temperature
parameter that, in the limit, concentrates the densities on permutation
matrices. We then exploit these transformations and reparameterization
gradients to introduce variational inference over permutation matrices, and we
demonstrate its utility in a series of experiments
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