2,524 research outputs found
Preconditioned fully implicit PDE solvers for monument conservation
Mathematical models for the description, in a quantitative way, of the
damages induced on the monuments by the action of specific pollutants are often
systems of nonlinear, possibly degenerate, parabolic equations. Although some
the asymptotic properties of the solutions are known, for a short window of
time, one needs a numerical approximation scheme in order to have a
quantitative forecast at any time of interest. In this paper a fully implicit
numerical method is proposed, analyzed and numerically tested for parabolic
equations of porous media type and on a systems of two PDEs that models the
sulfation of marble in monuments. Due to the nonlinear nature of the underlying
mathematical model, the use of a fixed point scheme is required and every step
implies the solution of large, locally structured, linear systems. A special
effort is devoted to the spectral analysis of the relevant matrices and to the
design of appropriate iterative or multi-iterative solvers, with special
attention to preconditioned Krylov methods and to multigrid procedures.
Numerical experiments for the validation of the analysis complement this
contribution.Comment: 26 pages, 13 figure
Noise reduction in coarse bifurcation analysis of stochastic agent-based models: an example of consumer lock-in
We investigate coarse equilibrium states of a fine-scale, stochastic
agent-based model of consumer lock-in in a duopolistic market. In the model,
agents decide on their next purchase based on a combination of their personal
preference and their neighbours' opinions. For agents with independent
identically-distributed parameters and all-to-all coupling, we derive an
analytic approximate coarse evolution-map for the expected average purchase. We
then study the emergence of coarse fronts when spatial segregation is present
in the relative perceived quality of products. We develop a novel Newton-Krylov
method that is able to compute accurately and efficiently coarse fixed points
when the underlying fine-scale dynamics is stochastic. The main novelty of the
algorithm is in the elimination of the noise that is generated when estimating
Jacobian-vector products using time-integration of perturbed initial
conditions. We present numerical results that demonstrate the convergence
properties of the numerical method, and use the method to show that macroscopic
fronts in this model destabilise at a coarse symmetry-breaking bifurcation.Comment: This version of the manuscript was accepted for publication on SIAD
Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module
The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project
The contact dynamics method for granular media
In this paper we review the simulation method of the non-smooth contact
dynamics. This technique was designed to solve the unilateral and frictional
contact problem for a large number of rigid bodies and has proved to be
especially valuable in research of dense granular materials during the last
decade. We present here the basic principles compared to other methods and the
detailed description of a 3D algorithm. We point out an artifact manifesting
itself in spurious sound waves and discuss the applicability of the method.Comment: for the proceedings of the 7th Granada Seminar, 23 pages, 8 figure
Newton-type methods under generalized self-concordance and inexact oracles
Many modern applications in machine learning, image/signal processing, and statistics require to solve large-scale convex optimization problems. These problems share some common challenges such as high-dimensionality, nonsmoothness, and complex objectives and constraints. Due to these challenges, the theoretical assumptions for existing numerical methods are not satisfied. In numerical methods, it is also impractical to do exact computations in many cases (e.g. noisy computation, storage or time limitation). Therefore, new approaches as well as inexact computations to design new algorithms should be considered. In this thesis, we develop fundamental theories and numerical methods, especially second-order methods, to solve some classes of convex optimization problems, where first-order methods are inefficient or do not have a theoretical guarantee. We aim at exploiting the underlying smoothness structures of the problem to design novel Newton-type methods. More specifically, we generalize a powerful concept called \mbox{self-concordance} introduced by Nesterov and Nemirovski to a broader class of convex functions. We develop several basic properties of this concept and prove key estimates for function values and its derivatives. Then, we apply our theory to design different Newton-type methods such as damped-step Newton methods, full-step Newton methods, and proximal Newton methods. Our new theory allows us to establish both global and local convergence guarantees of these methods without imposing unverifiable conditions as in classical Newton-type methods. Numerical experiments show that our approach has several advantages compared to existing works. In the second part of this thesis, we introduce new global and local inexact oracle settings, and apply them to develop inexact proximal Newton-type schemes for optimizing general composite convex problems equipped with such inexact oracles. These schemes allow us to measure errors theoretically and systematically and still lead to desired convergence results. Moreover, they can be applied to solve a wider class of applications arising in statistics and machine learning.Doctor of Philosoph
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