'Search direction' diaporamas de présentation

SYSTEMS Identification

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CSE 245: Computer Aided Circuit Simulation and Verification

CSE 245: Computer Aided Circuit Simulation and Verification. Fall 2004, Oct 19 Lecture 7: Matrix Solver II -Iterative Method. Outline. Iterative Method Stationary Iterative Method (SOR, GS,Jacob) Krylov Method (CG, GMRES) Multigrid Method. Iterative Methods. Stationary:

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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

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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

605 views • 60 slides

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

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Chapter 6

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Sub-Optimal Solutions for Satellite Orbital Transfers

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L22 Numerical Methods part 2

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Computation on meshes, sparse matrices, and graphs

Computation on meshes, sparse matrices, and graphs. Some slides are from David Culler, Jim Demmel, Bob Lucas, Horst Simon, Kathy Yelick, et al., UCB CS267. Parallelizing Stencil Computations. Parallelism is simple Grid is a regular data structure

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Searching a Linear Subspace

Searching a Linear Subspace. Lecture VI. Deriving Subspaces. There are several ways to derive the nullspace matrix (or kernel matrix). The methodology developed on in our last meeting is referred to the Variable Reduction Technique . The nullspace is then defined as

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Chapter 10 Minimization or Maximization of Functions

Chapter 10 Minimization or Maximization of Functions. Optimization Problems. Solution of equations can be formulated as an optimization problem, e.g., density functional theory (DFT) in electronic structure, Lagrangian mechanics, etc

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Performance Optimization

Performance Optimization. Basic Optimization Algorithm. or. p k - Search Direction. a k - Learning Rate. Steepest Descent. Choose the next step so that the function decreases:. For small changes in x we can approximate F ( x ):. where. If we want the function to decrease:.

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The Landscape of Ax=b Solvers

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Conjugate gradient iteration

Conjugate gradient iteration. x 0 = 0, r 0 = b, d 0 = r 0 for k = 1, 2, 3, . . . ? k = (r T k-1 r k-1 ) / (d T k-1 Ad k-1 ) step length x k = x k-1 + ? k d k-1 approx solution

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Introduction to Simulated Annealing

Introduction to Simulated Annealing. Study Guide for ES205 Yu-Chi Ho Xiaocang Lin Aug. 22, 2000. N. Difficulty in Searching Global Optima. barrier to local search. starting point. descend direction. local minima. global minima. N. Intuition of Simulated Annealing. Origin :.

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Final focus system optimization

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