Objective acceleration for unconstrained optimization

Acceleration schemes can dramatically improve existing optimization procedures. In most of the work on these schemes, such as nonlinear Generalized Minimal Residual (N-GMRES), acceleration is based on minimizing the $\ell_2$ norm of some target on subspaces of $\mathbb{R}^n$. There are many numerical examples that show how accelerating general purpose and domain-specific optimizers with N-GMRES results in large improvements. We propose a natural modification to N-GMRES, which significantly improves the performance in a testing environment originally used to advocate N-GMRES. Our proposed approach, which we refer to as O-ACCEL (Objective Acceleration), is novel in that it minimizes an approximation to the \emph{objective function} on subspaces of $\mathbb{R}^n$. We prove that O-ACCEL reduces to the Full Orthogonalization Method for linear systems when the objective is quadratic, which differentiates our proposed approach from existing acceleration methods. Comparisons with L-BFGS and N-CG indicate the competitiveness of O-ACCEL. As it can be combined with domain-specific optimizers, it may also be beneficial in areas where L-BFGS or N-CG are not suitable.Comment: 18 pages, 6 figures, 5 table

A symmetric rank-one Quasi-Newton line-search method using negative curvature directions

We propose a quasi-Newton line-search method that uses negative curvature directions for solving unconstrained optimization problems. In this method, the symmetric rank-one (SR1) rule is used to update the Hessian approximation. The SR1 update rule is known to have a good numerical performance; however, it does not guarantee positive definiteness of the updated matrix. We first discuss the details of the proposed algorithm and then concentrate on its numerical efficiency. Our extensive computational study shows the potential of the proposed method from different angles, such as; its second order convergence behavior, its exceeding performance when compared to two other existing packages, and its computation profile illustrating the possible bottlenecks in the execution time. We then conclude the paper with the convergence analysis of the proposed method

Effects of Non-Local Diffusion on Structural MRI Preprocessing and Default Network Mapping: Statistical Comparisons with Isotropic/Anisotropic Diffusion

Neuroimaging community usually employs spatial smoothing to denoise magnetic resonance imaging (MRI) data, e.g., Gaussian smoothing kernels. Such an isotropic diffusion (ISD) based smoothing is widely adopted for denoising purpose due to its easy implementation and efficient computation. Beyond these advantages, Gaussian smoothing kernels tend to blur the edges, curvature and texture of images. Researchers have proposed anisotropic diffusion (ASD) and non-local diffusion (NLD) kernels. We recently demonstrated the effect of these new filtering paradigms on preprocessing real degraded MRI images from three individual subjects. Here, to further systematically investigate the effects at a group level, we collected both structural and functional MRI data from 23 participants. We first evaluated the three smoothing strategies' impact on brain extraction, segmentation and registration. Finally, we investigated how they affect subsequent mapping of default network based on resting-state functional MRI (R-fMRI) data. Our findings suggest that NLD-based spatial smoothing maybe more effective and reliable at improving the quality of both MRI data preprocessing and default network mapping. We thus recommend NLD may become a promising method of smoothing structural MRI images of R-fMRI pipeline

High-Performance Computing Two-Scale Finite Element Simulations of a Contact Problem Using Computational Homogenization - Virtual Forming Limit Curves for Dual-Phase Steel

The appreciated macroscopic properties of dual-phase (DP) steels strongly depend on their microstructure. Therefore, accurate finite element (FE) simulations of a deformation process of such a steel require the incorporation of the microscopic heterogeneous structure. Usually, a brute force FE discretization incorporating the microstructure is not feasible since it results in exceedingly large problem sizes. Instead, the microstructure has to be incorporated by using computational homogenization. We present a numerical two-scale approach of the Nakajima test for a DP steel, which is a well known material test in the steel industry. It can be used to derive forming limit diagrams (FLDs), which allow experts to judge the maximum formability properties of a specific type of sheet metal in the considered thickness. For the simulations, we use our software package FE2TI, which is a highly scalable implementation of the well known FE2 homogenization approach. The microstructure is represented by a representative volume element (RVE) and it is discretized separately from the macroscopic problem. We discuss the incorporation of contact constraints using a penalty formulation as well as appropriate boundary conditions. In addition, we introduce a simple load step strategy and different opportunities for the choice of an initial value for a single load step by using an interpolation polynomial. Finally, we come up with computationally derived FLDs. Although we use a computational homogenization strategy, the resulting problems on both scales can be quite large. The efficient solution of such large problems requires parallel strategies. Therefore, we consider the highly scalable nonlinear domain decomposition methods FETI-DP (Finite Element Tearing and Interconnecting - Dual-Primal) and BDDC (Balancing Domain Decomposition by Constraints). For the first time, the BDDC approach is used for the parallel solution of the macroscopic problem in a simulation of the Nakajima test. We introduce a unified framework that combines all variants of nonlinear FETI-DP and nonlinear BDDC. For the first time, we introduce a nonlinear FETI-DP variant that chooses suitable elimination sets by utilizing information from the nonlinear residual. Furthermore, we show weak scaling results for different nonlinear FETI-DP variants and several model problems