4,101 research outputs found
Formulation, existence, and computation of boundedly rational dynamic user equilibrium with fixed or endogenous user tolerance
This paper analyzes dynamic user equilibrium (DUE) that incorporates the notion of boundedly rational (BR) user behavior in the selection of departure times and routes. Intrinsically, the boundedly rational dynamic user equilibrium (BR-DUE) model we present assumes that travelers do not always seek the least costly route-and-departure-time choice. Rather, their perception of travel cost is affected by an indifference band describing travelers’ tolerance of the difference between their experienced travel costs and the minimum travel cost. An extension of the BR-DUE problem is the so-called variable tolerance dynamic user equilibrium (VT-BR-DUE) wherein endogenously determined tolerances may depend not only on paths, but also on the established path departure rates. This paper presents a unified approach for modeling both BR-DUE and VT-BR-DUE, which makes significant contributions to the model formulation, analysis of existence, solution characterization, and numerical computation of such problems. The VT-BR-DUE problem, together with the BR-DUE problem as a special case, is formulated as a variational inequality. We provide a very general existence result for VT-BR-DUE and BR-DUE that relies on assumptions weaker than those required for normal DUE models. Moreover, a characterization of the solution set is provided based on rigorous topological analysis. Finally, three computational algorithms with convergence results are proposed based on the VI and DVI formulations. Numerical studies are conducted to assess the proposed algorithms in terms of solution quality, convergence, and computational efficiency
A computational comparison of two simplicial decomposition approaches for the separable traffic assignment problems : RSDTA and RSDVI
Draft pel 4th Meeting del Euro Working Group on Transportation (Newcastle 9-11 setembre de 1.996)The class of simplicial decomposition methods has shown to constitute efficient tools for the solution of the variational inequality formulation of the general traffic assignment problem. The paper presents a particular implementation of such an algorithm, called RSDVI, and a restricted simplicial decomposition algorithm, developed adhoc for diagonal, separable, problems named RSDTA. Both computer codes are compared for large scale separable traffic assignment problems. Some meaningful figures are shown for general problems with several levels of asymmetry.Preprin
Metastable states, quasi-stationary distributions and soft measures
We establish metastability in the sense of Lebowitz and Penrose under
practical and simple hypothesis for (families of) Markov chains on finite
configuration space in some asymptotic regime, including the case of
configuration space size going to infinity. By comparing restricted ensemble
and quasi-stationary measures, we study point-wise convergence velocity of
Yaglom limits and prove asymptotic exponential exit law. We introduce soft
measures as interpolation between restricted ensemble and quasi-stationary
measure to prove an asymptotic exponential transition law on a generally
different time scale. By using potential theoretic tools, we prove a new
general Poincar\'e inequality and give sharp estimates via two-sided
variational principles on relaxation time as well as mean exit time and
transition time. We also establish local thermalization on a shorter time scale
and give mixing time asymptotics up to a constant factor through a two-sided
variational principal. All our asymptotics are given with explicit quantitative
bounds on the corrective terms.Comment: 41 page
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
A central problem in machine learning involves modeling complex data-sets
using highly flexible families of probability distributions in which learning,
sampling, inference, and evaluation are still analytically or computationally
tractable. Here, we develop an approach that simultaneously achieves both
flexibility and tractability. The essential idea, inspired by non-equilibrium
statistical physics, is to systematically and slowly destroy structure in a
data distribution through an iterative forward diffusion process. We then learn
a reverse diffusion process that restores structure in data, yielding a highly
flexible and tractable generative model of the data. This approach allows us to
rapidly learn, sample from, and evaluate probabilities in deep generative
models with thousands of layers or time steps, as well as to compute
conditional and posterior probabilities under the learned model. We
additionally release an open source reference implementation of the algorithm
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