12,231 research outputs found
A Proximal Algorithm for Sampling
We study sampling problems associated with potentials that lack smoothness.
The potentials can be either convex or non-convex. Departing from the standard
smooth setting, the potentials are only assumed to be weakly smooth or
non-smooth, or the summation of multiple such functions. We develop a sampling
algorithm that resembles proximal algorithms in optimization for this
challenging sampling task. Our algorithm is based on a special case of Gibbs
sampling known as the alternating sampling framework (ASF). The key
contribution of this work is a practical realization of the ASF based on
rejection sampling for both non-convex and convex potentials that are not
necessarily smooth. In almost all the cases of sampling considered in this
work, our proximal sampling algorithm achieves better complexity than all
existing methods.Comment: 26 page
Colloquium: Trapped ions as quantum bits -- essential numerical tools
Trapped, laser-cooled atoms and ions are quantum systems which can be
experimentally controlled with an as yet unmatched degree of precision. Due to
the control of the motion and the internal degrees of freedom, these quantum
systems can be adequately described by a well known Hamiltonian. In this
colloquium, we present powerful numerical tools for the optimization of the
external control of the motional and internal states of trapped neutral atoms,
explicitly applied to the case of trapped laser-cooled ions in a segmented
ion-trap. We then delve into solving inverse problems, when optimizing trapping
potentials for ions. Our presentation is complemented by a quantum mechanical
treatment of the wavepacket dynamics of a trapped ion. Efficient numerical
solvers for both time-independent and time-dependent problems are provided.
Shaping the motional wavefunctions and optimizing a quantum gate is realized by
the application of quantum optimal control techniques. The numerical methods
presented can also be used to gain an intuitive understanding of quantum
experiments with trapped ions by performing virtual simulated experiments on a
personal computer. Code and executables are supplied as supplementary online
material (http://kilian-singer.de/ent).Comment: accepted for publication in Review of Modern Physics 201
Efficient computation of highly oscillatory integrals by using QTT tensor approximation
We propose a new method for the efficient approximation of a class of highly
oscillatory weighted integrals where the oscillatory function depends on the
frequency parameter , typically varying in a large interval. Our
approach is based, for fixed but arbitrary oscillator, on the pre-computation
and low-parametric approximation of certain -dependent prototype
functions whose evaluation leads in a straightforward way to recover the target
integral. The difficulty that arises is that these prototype functions consist
of oscillatory integrals and are itself oscillatory which makes them both
difficult to evaluate and to approximate. Here we use the quantized-tensor
train (QTT) approximation method for functional -vectors of logarithmic
complexity in in combination with a cross-approximation scheme for TT
tensors. This allows the accurate approximation and efficient storage of these
functions in the wide range of grid and frequency parameters. Numerical
examples illustrate the efficiency of the QTT-based numerical integration
scheme on various examples in one and several spatial dimensions.Comment: 20 page
High-order, Dispersionless "Fast-Hybrid" Wave Equation Solver. Part I: Sampling Cost via Incident-Field Windowing and Recentering
This paper proposes a frequency/time hybrid integral-equation method for the
time dependent wave equation in two and three-dimensional spatial domains.
Relying on Fourier Transformation in time, the method utilizes a fixed
(time-independent) number of frequency-domain integral-equation solutions to
evaluate, with superalgebraically-small errors, time domain solutions for
arbitrarily long times. The approach relies on two main elements, namely, 1) A
smooth time-windowing methodology that enables accurate band-limited
representations for arbitrarily-long time signals, and 2) A novel Fourier
transform approach which, in a time-parallel manner and without causing
spurious periodicity effects, delivers numerically dispersionless
spectrally-accurate solutions. A similar hybrid technique can be obtained on
the basis of Laplace transforms instead of Fourier transforms, but we do not
consider the Laplace-based method in the present contribution. The algorithm
can handle dispersive media, it can tackle complex physical structures, it
enables parallelization in time in a straightforward manner, and it allows for
time leaping---that is, solution sampling at any given time at
-bounded sampling cost, for arbitrarily large values of ,
and without requirement of evaluation of the solution at intermediate times.
The proposed frequency-time hybridization strategy, which generalizes to any
linear partial differential equation in the time domain for which
frequency-domain solutions can be obtained (including e.g. the time-domain
Maxwell equations), and which is applicable in a wide range of scientific and
engineering contexts, provides significant advantages over other available
alternatives such as volumetric discretization, time-domain integral equations,
and convolution-quadrature approaches.Comment: 33 pages, 8 figures, revised and extended manuscript (and now
including direct comparisons to existing CQ and TDIE solver implementations)
(Part I of II
A conjugate gradient minimisation approach to generating holographic traps for ultracold atoms
Direct minimisation of a cost function can in principle provide a versatile
and highly controllable route to computational hologram generation. However, to
date iterative Fourier transform algorithms have been predominantly used. Here
we show that the careful design of cost functions, combined with numerically
efficient conjugate gradient minimisation, establishes a practical method for
the generation of holograms for a wide range of target light distributions.
This results in a guided optimisation process, with a crucial advantage
illustrated by the ability to circumvent optical vortex formation during
hologram calculation. We demonstrate the implementation of the conjugate
gradient method for both discrete and continuous intensity distributions and
discuss its applicability to optical trapping of ultracold atoms.Comment: 11 pages, 4 figure
Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems
Optimization methods are at the core of many problems in signal/image
processing, computer vision, and machine learning. For a long time, it has been
recognized that looking at the dual of an optimization problem may drastically
simplify its solution. Deriving efficient strategies which jointly brings into
play the primal and the dual problems is however a more recent idea which has
generated many important new contributions in the last years. These novel
developments are grounded on recent advances in convex analysis, discrete
optimization, parallel processing, and non-smooth optimization with emphasis on
sparsity issues. In this paper, we aim at presenting the principles of
primal-dual approaches, while giving an overview of numerical methods which
have been proposed in different contexts. We show the benefits which can be
drawn from primal-dual algorithms both for solving large-scale convex
optimization problems and discrete ones, and we provide various application
examples to illustrate their usefulness
Nesterov smoothing for sampling without smoothness
We study the problem of sampling from a target distribution in
whose potential is not smooth. Compared with the sampling problem with smooth
potentials, this problem is much less well-understood due to the lack of
smoothness. In this paper, we propose a novel sampling algorithm for a class of
non-smooth potentials by first approximating them by smooth potentials using a
technique that is akin to Nesterov smoothing. We then utilize sampling
algorithms on the smooth potentials to generate approximate samples from the
original non-smooth potentials. We select an appropriate smoothing intensity to
ensure that the distance between the smoothed and un-smoothed distributions is
minimal, thereby guaranteeing the algorithm's accuracy. Hence we obtain
non-asymptotic convergence results based on existing analysis of smooth
sampling. We verify our convergence result on a synthetic example and apply our
method to improve the worst-case performance of Bayesian inference on a
real-world example
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