26,954 research outputs found
Sampling-based Approximations with Quantitative Performance for the Probabilistic Reach-Avoid Problem over General Markov Processes
This article deals with stochastic processes endowed with the Markov
(memoryless) property and evolving over general (uncountable) state spaces. The
models further depend on a non-deterministic quantity in the form of a control
input, which can be selected to affect the probabilistic dynamics. We address
the computation of maximal reach-avoid specifications, together with the
synthesis of the corresponding optimal controllers. The reach-avoid
specification deals with assessing the likelihood that any finite-horizon
trajectory of the model enters a given goal set, while avoiding a given set of
undesired states. This article newly provides an approximate computational
scheme for the reach-avoid specification based on the Fitted Value Iteration
algorithm, which hinges on random sample extractions, and gives a-priori
computable formal probabilistic bounds on the error made by the approximation
algorithm: as such, the output of the numerical scheme is quantitatively
assessed and thus meaningful for safety-critical applications. Furthermore, we
provide tighter probabilistic error bounds that are sample-based. The overall
computational scheme is put in relationship with alternative approximation
algorithms in the literature, and finally its performance is practically
assessed over a benchmark case study
Stochastic Programming with Probability
In this work we study optimization problems subject to a failure constraint.
This constraint is expressed in terms of a condition that causes failure,
representing a physical or technical breakdown. We formulate the problem in
terms of a probability constraint, where the level of "confidence" is a
modelling parameter and has the interpretation that the probability of failure
should not exceed that level. Application of the stochastic Arrow-Hurwicz
algorithm poses two difficulties: one is structural and arises from the lack of
convexity of the probability constraint, and the other is the estimation of the
gradient of the probability constraint. We develop two gradient estimators with
decreasing bias via a convolution method and a finite difference technique,
respectively, and we provide a full analysis of convergence of the algorithms.
Convergence results are used to tune the parameters of the numerical algorithms
in order to achieve best convergence rates, and numerical results are included
via an example of application in finance
Sparse Deterministic Approximation of Bayesian Inverse Problems
We present a parametric deterministic formulation of Bayesian inverse
problems with input parameter from infinite dimensional, separable Banach
spaces. In this formulation, the forward problems are parametric, deterministic
elliptic partial differential equations, and the inverse problem is to
determine the unknown, parametric deterministic coefficients from noisy
observations comprising linear functionals of the solution.
We prove a generalized polynomial chaos representation of the posterior
density with respect to the prior measure, given noisy observational data. We
analyze the sparsity of the posterior density in terms of the summability of
the input data's coefficient sequence. To this end, we estimate the
fluctuations in the prior. We exhibit sufficient conditions on the prior model
in order for approximations of the posterior density to converge at a given
algebraic rate, in terms of the number of unknowns appearing in the
parameteric representation of the prior measure. Similar sparsity and
approximation results are also exhibited for the solution and covariance of the
elliptic partial differential equation under the posterior. These results then
form the basis for efficient uncertainty quantification, in the presence of
data with noise
Sequential Design for Optimal Stopping Problems
We propose a new approach to solve optimal stopping problems via simulation.
Working within the backward dynamic programming/Snell envelope framework, we
augment the methodology of Longstaff-Schwartz that focuses on approximating the
stopping strategy. Namely, we introduce adaptive generation of the stochastic
grids anchoring the simulated sample paths of the underlying state process.
This allows for active learning of the classifiers partitioning the state space
into the continuation and stopping regions. To this end, we examine sequential
design schemes that adaptively place new design points close to the stopping
boundaries. We then discuss dynamic regression algorithms that can implement
such recursive estimation and local refinement of the classifiers. The new
algorithm is illustrated with a variety of numerical experiments, showing that
an order of magnitude savings in terms of design size can be achieved. We also
compare with existing benchmarks in the context of pricing multi-dimensional
Bermudan options.Comment: 24 page
The mean-field approximation and the non-linear Schr\"odinger functional for trapped Bose gases
We study the ground state of a trapped Bose gas, starting from the full
many-body Schr{\"o}dinger Hamiltonian, and derive the nonlinear Schr{\"o}dinger
energy functional in the limit of large particle number, when the interaction
potential converges slowly to a Dirac delta function. Our method is based on
quantitative estimates on the discrepancy between the full many-body energy and
its mean-field approximation using Hartree states. These are proved using
finite dimensional localization and a quantitative version of the quantum de
Finetti theorem. Our approach covers the case of attractive interactions in the
regime of stability. In particular, our main new result is a derivation of the
2D attractive nonlinear Schr{\"o}dinger ground state
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