A Subgradient Method for Free Material Design

A small improvement in the structure of the material could save the manufactory a lot of money. The free material design can be formulated as an optimization problem. However, due to its large scale, second-order methods cannot solve the free material design problem in reasonable size. We formulate the free material optimization (FMO) problem into a saddle-point form in which the inverse of the stiffness matrix A(E) in the constraint is eliminated. The size of A(E) is generally large, denoted as N by N. This is the first formulation of FMO without A(E). We apply the primal-dual subgradient method [17] to solve the restricted saddle-point formula. This is the first gradient-type method for FMO. Each iteration of our algorithm takes a total of $O(N^2)$ foating-point operations and an auxiliary vector storage of size O(N), compared with formulations having the inverse of A(E) which requires $O(N^3)$ arithmetic operations and an auxiliary vector storage of size $O(N^2)$. To solve the problem, we developed a closed-form solution to a semidefinite least squares problem and an efficient parameter update scheme for the gradient method, which are included in the appendix. We also approximate a solution to the bounded Lagrangian dual problem. The problem is decomposed into small problems each only having an unknown of k by k (k = 3 or 6) matrix, and can be solved in parallel. The iteration bound of our algorithm is optimal for general subgradient scheme. Finally we present promising numerical results.Comment: SIAM Journal on Optimization (accepted

Iterated preconditioned LSQR method for inverse problems on unstructured grids

This article presents a method for solving large-scale linear inverse imaging problems regularized with a nonlinear, edge-preserving penalty term such as total variation or the Perona–Malik technique. Our method is aimed at problems defined on unstructured meshes, where such regularizers naturally arise in unfactorized form as a stiffness matrix of an anisotropic diffusion operator and factorization is prohibitively expensive. In the proposed scheme, the nonlinearity is handled with lagged diffusivity fixed point iteration, which involves solving a large-scale linear least squares problem in each iteration. Because the convergence of Krylov methods for problems with discontinuities is notoriously slow, we propose to accelerate it by means of priorconditioning (Bayesian preconditioning). priorconditioning is a technique that, through transformation to the standard form, embeds the information contained in the prior (Bayesian interpretation of a regularizer) directly into the forward operator and thence into the solution space. We derive a factorization-free preconditioned LSQR algorithm (MLSQR), allowing implicit application of the preconditioner through efficient schemes such as multigrid. The resulting method is also matrix-free i.e. the forward map can be defined through its action on a vector. We illustrate the performance of the method on two numerical examples. Simple 1D-deblurring problem serves to visualize the discussion throughout the paper. The effectiveness of the proposed numerical scheme is demonstrated on a three-dimensional problem in fluorescence diffuse optical tomography with total variation regularization derived algebraic multigrid preconditioner, which is the type of large scale, unstructured mesh problem, requiring matrix-free and factorization-free approaches that motivated the work here

Cell-based maximum entropy approximants for three-dimensional domains: Application in large strain elastodynamics using the meshless total Lagrangian explicit dynamics method

We present the cell-based maximum entropy (CME) approximants in E3 space by constructing the smooth approximation distance function to polyhedral surfaces. CME is a meshfree approximation method combining the properties of the maximum entropy approximants and the compact support of element-based interpolants. The method is evaluated in problems of large strain elastodynamics for three-dimensional (3D) continua using the well-established meshless total Lagrangian explicit dynamics method. The accuracy and efficiency of the method is assessed in several numerical examples in terms of computational time, accuracy in boundary conditions imposition, and strain energy density error. Due to the smoothness of CME basis functions, the numerical stability in explicit time integration is preserved for large time step. The challenging task of essential boundary condition (EBC) imposition in noninterpolating meshless methods (eg, moving least squares) is eliminated in CME due to the weak Kronecker-delta property. The EBCs are imposed directly, similar to the finite element method. CME is proven a valuable alternative to other meshless and element-based methods for large-scale elastodynamics in 3D. A naive implementation of the CME approximants in E3 is available to download at https://www.mountris.org/software/mlab/cme.Fil: Mountris, Konstantinos A.. Universidad de Zaragoza; EspañaFil: Bourantas, George C.. University of Western Australia; AustraliaFil: Millán, Raúl Daniel. Universidad Nacional de Cuyo. Facultad de Ciencias Aplicadas a la Industria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Joldes, Grand R.. University of Western Australia; AustraliaFil: Miller, Karol. Cardiff University; Reino Unido. University of Western Australia; AustraliaFil: Pueyo, Esther. Centro de Investigacion Biomedica En Red.; España. Universidad de Zaragoza; EspañaFil: Wittek, Adam. University of Western Australia; Australi

Three-dimensional inversion of transient-electromagnetic data: A comparative study

Inversion of transient-electromagnetic (TEM) data arising from galvanic types of sources is approached by two different methods. Both methods reconstruct the subsurface three-dimensional (3D) electrical conductivity properties directly in the time-domain. A principal difference is given by the scale of the inversion problems to be solved. The first approach represents a small-scale 3D inversion and is based upon well-known tools. It uses a stabilized unconstrained least-squares inversion algorithm in combination with an existing 3D forward modeling solver and is customized to invert for 3D earth models with a limited model complexity. The limitation to only as many model unknowns as typical for classical least-squares problems involves arbitrary and rather unconventional types of model parameters. The inversion scheme has mainly been developed for the purpose of refining a priori known 3D underground structures by means of an inversion. Therefore, a priori information is an important requirement to design a model such that its limited degrees of freedom describe the structures of interest. The inversion is successfully applied to data from a long-offset TEM survey at the active volcano Merapi in Central Java (Indonesia). Despite the restriction of a low model complexity, the scheme offers some versatility as it can be adapted easily to various kinds of model structures. The interpretation of the resistivity images obtained by the inversion have substantially advanced the structural knowledge about the volcano. The second part of this work presents a theoretically more elaborate scheme. It employs imaging techniques originally developed for seismic wavefields. Large-scale 3D problems arising from the inversion for finely parameterized and arbitrarily complicated earth models are addressed by the method. The algorithm uses a conjugate-gradient search for the minimum of an error functional, where the gradient information is obtained via migration or backpropagation of the differences between the data observations and predictions back into the model in reverse time. Treatment for electric field and time derivative of the magnetic field data is given for the specification of the cost functional gradients. The inversion algorithm is successfully applied to a synthetic TEM data set over a conductive anomaly embedded in a half-space. The example involves a total number of more than 376000 model unknowns. The realization of migration techniques for diffusive EM fields involves the backpropagation of a residual field. The residual field excitation originates from the actual receiver positions and is continued during the simulated time range of the measurements. An explicit finite-difference time-stepping scheme is developed in advance of the imaging scheme in order to accomplish both the forward simulation and backpropagation of 3D EM fields. The solution uses a staggered grid and a modified version of the DuFort-Frankel stabilization method and is capable of simulating non-causal fields due to galvanic types of sources. Its parallel implementation allows for reasonable computation times, which are inherently high for explicit time-stepping schemes