23,730 research outputs found
Non-Reversible Parallel Tempering: a Scalable Highly Parallel MCMC Scheme
Parallel tempering (PT) methods are a popular class of Markov chain Monte
Carlo schemes used to sample complex high-dimensional probability
distributions. They rely on a collection of interacting auxiliary chains
targeting tempered versions of the target distribution to improve the
exploration of the state-space. We provide here a new perspective on these
highly parallel algorithms and their tuning by identifying and formalizing a
sharp divide in the behaviour and performance of reversible versus
non-reversible PT schemes. We show theoretically and empirically that a class
of non-reversible PT methods dominates its reversible counterparts and identify
distinct scaling limits for the non-reversible and reversible schemes, the
former being a piecewise-deterministic Markov process and the latter a
diffusion. These results are exploited to identify the optimal annealing
schedule for non-reversible PT and to develop an iterative scheme approximating
this schedule. We provide a wide range of numerical examples supporting our
theoretical and methodological contributions. The proposed methodology is
applicable to sample from a distribution with a density with respect
to a reference distribution and compute the normalizing constant. A
typical use case is when is a prior distribution, a likelihood
function and the corresponding posterior.Comment: 74 pages, 30 figures. The method is implemented in an open source
probabilistic programming available at
https://github.com/UBC-Stat-ML/blangSD
Practical Volume Estimation by a New Annealing Schedule for Cooling Convex Bodies
We study the problem of estimating the volume of convex polytopes, focusing
on H- and V-polytopes, as well as zonotopes. Although a lot of effort is
devoted to practical algorithms for H-polytopes there is no such method for the
latter two representations. We propose a new, practical algorithm for all
representations, which is faster than existing methods. It relies on
Hit-and-Run sampling, and combines a new simulated annealing method with the
Multiphase Monte Carlo (MMC) approach. Our method introduces the following key
features to make it adaptive: (a) It defines a sequence of convex bodies in MMC
by introducing a new annealing schedule, whose length is shorter than in
previous methods with high probability, and the need of computing an enclosing
and an inscribed ball is removed; (b) It exploits statistical properties in
rejection-sampling and proposes a better empirical convergence criterion for
specifying each step; (c) For zonotopes, it may use a sequence of convex bodies
for MMC different than balls, where the chosen body adapts to the input. We
offer an open-source, optimized C++ implementation, and analyze its performance
to show that it outperforms state-of-the-art software for H-polytopes by
Cousins-Vempala (2016) and Emiris-Fisikopoulos (2018), while it undertakes
volume computations that were intractable until now, as it is the first
polynomial-time, practical method for V-polytopes and zonotopes that scales to
high dimensions (currently 100). We further focus on zonotopes, and
characterize them by their order (number of generators over dimension), because
this largely determines sampling complexity. We analyze a related application,
where we evaluate methods of zonotope approximation in engineering.Comment: 20 pages, 12 figures, 3 table
Variable Annealing Length and Parallelism in Simulated Annealing
In this paper, we propose: (a) a restart schedule for an adaptive simulated
annealer, and (b) parallel simulated annealing, with an adaptive and
parameter-free annealing schedule. The foundation of our approach is the
Modified Lam annealing schedule, which adaptively controls the temperature
parameter to track a theoretically ideal rate of acceptance of neighboring
states. A sequential implementation of Modified Lam simulated annealing is
almost parameter-free. However, it requires prior knowledge of the annealing
length. We eliminate this parameter using restarts, with an exponentially
increasing schedule of annealing lengths. We then extend this restart schedule
to parallel implementation, executing several Modified Lam simulated annealers
in parallel, with varying initial annealing lengths, and our proposed parallel
annealing length schedule. To validate our approach, we conduct experiments on
an NP-Hard scheduling problem with sequence-dependent setup constraints. We
compare our approach to fixed length restarts, both sequentially and in
parallel. Our results show that our approach can achieve substantial
performance gains, throughout the course of the run, demonstrating our approach
to be an effective anytime algorithm.Comment: Tenth International Symposium on Combinatorial Search, pages 2-10.
June 201
Simulated Annealing with Tsallis Weights - A Numerical Comparison
We discuss the use of Tsallis generalized mechanics in simulated annealing
algorithms. For a small peptide it is shown that older implementations are not
more effective than regular simulated annealing in finding ground state
configurations. We propose a new implementation which leads to an improvement
over regular simulated annealing.Comment: Late
Optimization by Quantum Annealing: Lessons from hard 3-SAT cases
The Path Integral Monte Carlo simulated Quantum Annealing algorithm is
applied to the optimization of a large hard instance of the Random 3-SAT
Problem (N=10000). The dynamical behavior of the quantum and the classical
annealing are compared, showing important qualitative differences in the way of
exploring the complex energy landscape of the combinatorial optimization
problem. At variance with the results obtained for the Ising spin glass and for
the Traveling Salesman Problem, in the present case the linear-schedule Quantum
Annealing performance is definitely worse than Classical Annealing.
Nevertheless, a quantum cooling protocol based on field-cycling and able to
outperform standard classical simulated annealing over short time scales is
introduced.Comment: 10 pages, 6 figures, submitted to PR
Readiness of Quantum Optimization Machines for Industrial Applications
There have been multiple attempts to demonstrate that quantum annealing and,
in particular, quantum annealing on quantum annealing machines, has the
potential to outperform current classical optimization algorithms implemented
on CMOS technologies. The benchmarking of these devices has been controversial.
Initially, random spin-glass problems were used, however, these were quickly
shown to be not well suited to detect any quantum speedup. Subsequently,
benchmarking shifted to carefully crafted synthetic problems designed to
highlight the quantum nature of the hardware while (often) ensuring that
classical optimization techniques do not perform well on them. Even worse, to
date a true sign of improved scaling with the number of problem variables
remains elusive when compared to classical optimization techniques. Here, we
analyze the readiness of quantum annealing machines for real-world application
problems. These are typically not random and have an underlying structure that
is hard to capture in synthetic benchmarks, thus posing unexpected challenges
for optimization techniques, both classical and quantum alike. We present a
comprehensive computational scaling analysis of fault diagnosis in digital
circuits, considering architectures beyond D-wave quantum annealers. We find
that the instances generated from real data in multiplier circuits are harder
than other representative random spin-glass benchmarks with a comparable number
of variables. Although our results show that transverse-field quantum annealing
is outperformed by state-of-the-art classical optimization algorithms, these
benchmark instances are hard and small in the size of the input, therefore
representing the first industrial application ideally suited for testing
near-term quantum annealers and other quantum algorithmic strategies for
optimization problems.Comment: 22 pages, 12 figures. Content updated according to Phys. Rev. Applied
versio
Application of Quantum Annealing to Nurse Scheduling Problem
Quantum annealing is a promising heuristic method to solve combinatorial
optimization problems, and efforts to quantify performance on real-world
problems provide insights into how this approach may be best used in practice.
We investigate the empirical performance of quantum annealing to solve the
Nurse Scheduling Problem (NSP) with hard constraints using the D-Wave 2000Q
quantum annealing device. NSP seeks the optimal assignment for a set of nurses
to shifts under an accompanying set of constraints on schedule and personnel.
After reducing NSP to a novel Ising-type Hamiltonian, we evaluate the solution
quality obtained from the D-Wave 2000Q against the constraint requirements as
well as the diversity of solutions. For the test problems explored here, our
results indicate that quantum annealing recovers satisfying solutions for NSP
and suggests the heuristic method is sufficient for practical use. Moreover, we
observe that solution quality can be greatly improved through the use of
reverse annealing, in which it is possible to refine a returned results by
using the annealing process a second time. We compare the performance NSP using
both forward and reverse annealing methods and describe how these approach
might be used in practice.Comment: 20 pages, 13 figure
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