'Conjugate gradient' presentation slideshows

CSE 245: Computer Aided Circuit Simulation and Verification

CSE 245: Computer Aided Circuit Simulation and Verification. Matrix Computations: Iterative Methods (II) Chung-Kuan Cheng. Outline. Introduction Direct Methods Iterative Methods Formulations Projection Methods Krylov Space Methods Preconditioned Iterations Multigrid Methods

724 views • 60 slides

Interatomic Potentials for Ionic Systems

1.56k views • 58 slides

Interatomic Potentials for Ionic Systems

962 views • 58 slides

By Thang Ha & Yuji Kim University of Oklahoma, 2016

Seismic Reprocessing: Constrained Conjugate Gradient Least-Squares Migration and Migration-Driven 5D Interpolation: Application to Jeju Basin Marine Data. By Thang Ha & Yuji Kim University of Oklahoma, 2016. Motivation. What do we do to get high quality images?. Expensive.

1.07k views • 52 slides

Summer School In Geophysical Porous Media July 17-28, 2006 Purdue University, West Lafayette, Indiana

MODELING FLOW IN VUGGY MEDIA. Advisor Todd Arbogast - Mathematics, University of Texas at Austin. Diana Battefeld - Physics, Brown University Cheng Chen - Engineering, Northwestern University Abdullah Cihan - Engineering, University of Tennessee, Knoxville

753 views • 52 slides

Chapter 4: Artificial Neural Networks

Chapter 4: Artificial Neural Networks. Artificial neural network(ANN) General, practical method for learning real-valued, discrete-valued, vector-valued functions from examples BACPROPAGATION ???? Use gradient descent to tune network parameters to best fit a training set of input-output pairs

713 views • 46 slides

Canonical Granger Causality Applied to Functional Brain Data

Canonical Granger Causality Applied to Functional Brain Data. Syed Ashrafulla, Justin P Haldar , Anand A Joshi, Richard M Leahy. Biomedical Imaging Group, Signal & Image Processing Institute, University of Southern California.

583 views • 39 slides

Support Vector Machines S.V.M. Special session

519 views • 37 slides

Computational Solid State Physics ???????????

Computational Solid State Physics ???????????. 7. Many-body effect I Hartree approximation, H artree-Fock approximation and Density functional method. Hartree approximation. N-electron Hamiltonian . ? N-electron wave function . i-th spin-orbit. ortho-normal set.

656 views • 33 slides

Nonsmooth DEM Simulation Fast and stable simulation of granular matter and machines

451 views • 32 slides

Mesh Parameterization: Theory and Practice Making it work in practice - Numerics

468 views • 32 slides

Sparse Matrix Methods

Sparse Matrix Methods. Day 1: Overview Day 2: Direct methods Day 3: Iterative methods The conjugate gradient algorithm Parallel conjugate gradient and graph partitioning Preconditioning methods and graph coloring Domain decomposition and multigrid Krylov subspace methods for other problems

485 views • 30 slides

Lecture 2c: Benchmarks

375 views • 26 slides

436 views • 25 slides

Data Flow Pattern Analysis of Scientific Applications

Data Flow Pattern Analysis of Scientific Applications . Michael Frumkin Parallel Systems & Applications Intel Corporation May 6, 2005. Outline. Why Data Flow Pattern Analysis? CFD Applications The NAS Parallel Benchmarks The NAS Grid Benchmarks Trace File Analysis Conclusions.

567 views • 24 slides

The linear system

The linear system. The problem: solve Suppose A is invertible, then there exists a unique solution How to efficiently compute the solution numerically???. Review of direct methods. Gaussian elimination with pivoting Memory cost: O(n^2) Computational cost: O(n^3)

474 views • 23 slides

Two-Dimensional P hase U nwrapping O n FPGAs And GPUs

Two-Dimensional P hase U nwrapping O n FPGAs And GPUs . Sherman Braganza Prof . Miriam Leeser ReConfigurable Laboratory Northeastern University Boston, MA. Outline. Introduction Motivation Optical Quadrature Microscopy Phase unwrapping Algorithms

348 views • 21 slides

Seven Minute Madness: Heterogeneous Computing

Seven Minute Madness: Heterogeneous Computing. Dr. Jason D. Bakos. CPU Design. FOR i = 0 to 1000 C[ i ] = A[ i ] + B[ i ]. Core 1. Core 2. CPU. Control. ALU. ALU. Control. ALU. ALU. ALU. ALU. ALU. ALU. time. L1 cache. L1 cache. Copy part of A onto CPU. L2 cache. L2 cache.

300 views • 20 slides

Gradient Projection for bound-constrained QPs

Gradient Projection for bound-constrained QPs. Sophea Chan May 29, 2012. Gradient Projection for bound-constrained QPs. Overview. B ound constrained QPs Gradient Projection Method Conjugate Gradient Method Projected Search Implementation. Sophea Chan.

480 views • 15 slides

g = Af g : Vector of projection data f : vector of image data A : matrix

Iterative Algorithm. g = Af g : Vector of projection data f : vector of image data A : matrix The principle of the iterative algorithms is to find a solution by successive estimates. Iterative Algorithm. Algorithms of General Concept of Iterative Methods:

291 views • 13 slides