46,011 research outputs found
Parallel implementation of an optimal two level additive Schwarz preconditioner for the 3-D finite element solution of elliptic partial differential equations
This paper presents a description of the extension and parallel implementation of a new two level additive Schwarz (AS) preconditioner for the solution of 3-D elliptic partial differential equations (PDEs). This preconditioner, introduced in Bank et al. (SIAM J. Sci. Comput. 2002; 23: 1818), is based upon the use of a novel form of overlap between the subdomains which makes use of a hierarchy of meshes: with just a single layer of overlapping elements at each level of the hierarchy. The generalization considered here is based upon the restricted AS approach reported in (SIAM J. Sci. Comput. 1999; 21: 792) and the parallel implementation is an extension of work in two dimensions (Concurrency Comput. Practice Experience 2001; 13: 327)
A multilevel approach for obtaining locally optimal finite element meshes
In this paper we consider the adaptive finite element solution of a general class of variational problems using a combination of node insertion, node movement and edge swapping. The adaptive strategy that is proposed is based upon the construction of a hierarchy of locally optimal meshes starting with a coarse grid for which the location and connectivity of the nodes is optimized.
This grid is then locally refined and the new mesh is optimized in the same manner. Results presented indicate that this approach is able to produce better meshes than those possible by more conventional adaptive strategies and in a relatively efficient manner
Optimization Algorithms Based on Renormalization Group
Global changes of states are of crucial importance in optimization
algorithms. We review some heuristic algorithms in which global updates are
realized by a sort of real-space renormalization group transformation. Emphasis
is on the relationship between the structure of low-energy excitations and
``block-spins'' appearing in the algorithms. We also discuss the implication of
existence of a finite-temperature phase transition on the computational
complexity of the ground-state problem.Comment: 7 pages, 2 figure
Hyperparameter Estimation in Bayesian MAP Estimation: Parameterizations and Consistency
The Bayesian formulation of inverse problems is attractive for three primary
reasons: it provides a clear modelling framework; means for uncertainty
quantification; and it allows for principled learning of hyperparameters. The
posterior distribution may be explored by sampling methods, but for many
problems it is computationally infeasible to do so. In this situation maximum a
posteriori (MAP) estimators are often sought. Whilst these are relatively cheap
to compute, and have an attractive variational formulation, a key drawback is
their lack of invariance under change of parameterization. This is a
particularly significant issue when hierarchical priors are employed to learn
hyperparameters. In this paper we study the effect of the choice of
parameterization on MAP estimators when a conditionally Gaussian hierarchical
prior distribution is employed. Specifically we consider the centred
parameterization, the natural parameterization in which the unknown state is
solved for directly, and the noncentred parameterization, which works with a
whitened Gaussian as the unknown state variable, and arises when considering
dimension-robust MCMC algorithms; MAP estimation is well-defined in the
nonparametric setting only for the noncentred parameterization. However, we
show that MAP estimates based on the noncentred parameterization are not
consistent as estimators of hyperparameters; conversely, we show that limits of
finite-dimensional centred MAP estimators are consistent as the dimension tends
to infinity. We also consider empirical Bayesian hyperparameter estimation,
show consistency of these estimates, and demonstrate that they are more robust
with respect to noise than centred MAP estimates. An underpinning concept
throughout is that hyperparameters may only be recovered up to measure
equivalence, a well-known phenomenon in the context of the Ornstein-Uhlenbeck
process.Comment: 36 pages, 8 figure
Locally optimal unstructured finite element meshes in 3 dimensions
This paper investigates the adaptive finite element solution of a general class of variational problems in three dimensions using a combination of node movement, edge swapping, face swapping and node insertion. The adaptive strategy proposed is a generalization of previous work in two dimensions and is based upon the construction of a hierarchy of locally optimal meshes.
Results presented, both for a single equation and a system of coupled equations, suggest that this approach is able to produce better meshes of tetrahedra than those obtained by more conventional adaptive strategies and in a relatively efficient manner
Gradient-Based Estimation of Uncertain Parameters for Elliptic Partial Differential Equations
This paper addresses the estimation of uncertain distributed diffusion
coefficients in elliptic systems based on noisy measurements of the model
output. We formulate the parameter identification problem as an infinite
dimensional constrained optimization problem for which we establish existence
of minimizers as well as first order necessary conditions. A spectral
approximation of the uncertain observations allows us to estimate the infinite
dimensional problem by a smooth, albeit high dimensional, deterministic
optimization problem, the so-called finite noise problem in the space of
functions with bounded mixed derivatives. We prove convergence of finite noise
minimizers to the appropriate infinite dimensional ones, and devise a
stochastic augmented Lagrangian method for locating these numerically. Lastly,
we illustrate our method with three numerical examples
Low-rank approximate inverse for preconditioning tensor-structured linear systems
In this paper, we propose an algorithm for the construction of low-rank
approximations of the inverse of an operator given in low-rank tensor format.
The construction relies on an updated greedy algorithm for the minimization of
a suitable distance to the inverse operator. It provides a sequence of
approximations that are defined as the projections of the inverse operator in
an increasing sequence of linear subspaces of operators. These subspaces are
obtained by the tensorization of bases of operators that are constructed from
successive rank-one corrections. In order to handle high-order tensors,
approximate projections are computed in low-rank Hierarchical Tucker subsets of
the successive subspaces of operators. Some desired properties such as symmetry
or sparsity can be imposed on the approximate inverse operator during the
correction step, where an optimal rank-one correction is searched as the tensor
product of operators with the desired properties. Numerical examples illustrate
the ability of this algorithm to provide efficient preconditioners for linear
systems in tensor format that improve the convergence of iterative solvers and
also the quality of the resulting low-rank approximations of the solution
A DC Programming Approach for Solving Multicast Network Design Problems via the Nesterov Smoothing Technique
This paper continues our effort initiated in [9] to study Multicast
Communication Networks, modeled as bilevel hierarchical clustering problems, by
using mathematical optimization techniques. Given a finite number of nodes, we
consider two different models of multicast networks by identifying a certain
number of nodes as cluster centers, and at the same time, locating a particular
node that serves as a total center so as to minimize the total transportation
cost through the network. The fact that the cluster centers and the total
center have to be among the given nodes makes this problem a discrete
optimization problem. Our approach is to reformulate the discrete problem as a
continuous one and to apply Nesterov smoothing approximation technique on the
Minkowski gauges that are used as distance measures. This approach enables us
to propose two implementable DCA-based algorithms for solving the problems.
Numerical results and practical applications are provided to illustrate our
approach
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