'Steepest descent' diaporamas de présentation

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Engineering Optimization and Nature

Engineering Optimization and Nature. Ender Ayanoglu Center for Pervasive Communications and Computing The Henry Samueli School of Engineering University of California, Irvine Calit2 UC Irvine Division Workshop on Biological and Computing/Communication Systems July 6, 2009.

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Dimensionality reduction: Some Assumptions

Dimensionality reduction: Some Assumptions. High-dimensional data often lies on or near a much lower dimensional, curved manifold. A good way to represent data points is by their low-dimensional coordinates.

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Steepest Descent Method

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L-BFGS and Delayed Dynamical Systems Approach for Unconstrained Optimization

L-BFGS and Delayed Dynamical Systems Approach for Unconstrained Optimization. Xiaohui XIE Supervisor: Dr. Hon Wah TAM. Outline. Problem background and introduction Analysis for dynamical systems with time delay Introduction of dynamical systems Delayed dynamical systems approach

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Rice University Department of Computational and Applied Mathematics

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296.3:Algorithms in the Real World

296.3:Algorithms in the Real World. Linear and Integer Programming II Ellipsoid algorithm Interior point methods. Ellipsoid Algorithm. First known-to-be polynomial-time algorithm for linear programming (Khachian 79) Solves find x subject to Ax ? b

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Materials and Methods: Computational methods

Unusual sequence effects on nucleotide excision repair of arylamine lesions: DNA bending/distortion as a primary recognition factor

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L-BFGS and Delayed Dynamical Systems Approach for Unconstrained Optimization

L-BFGS and Delayed Dynamical Systems Approach for Unconstrained Optimization. Xiaohui XIE Supervisor: Dr. Hon Wah TAM. Outline. Problem background and introduction Analysis for dynamical systems with time delay Introduction of dynamical systems Delayed dynamical systems approach

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Baseband Pulse Transmission

Baseband Pulse Transmission. Correlative-Level Coding Baseband M-ary PAM Transmission Tapped-Delay-Line Equalization Eye Pattern. Hyeong-Seok Yu Vada Lab. gargoyle@vada1.skku.ac.kr. Correlative-level coding (partial response signaling)

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Bending, Breaking and Squishing Stuff

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Scalable training of L 1 -regularized log-linear models

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Approaches for Retinex and Their Relations

Approaches for Retinex and Their Relations. Yu Du March 14, 2002. Presentation Outline. Introductions to retinex Approaches for retinex The variational framework Relation of these approaches Conclusions. What Is Retinex. Lightness and retinex theory E. H. Land 1971

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Linear Discrimination

Linear Discrimination. Reading: Chapter 2 of textbook. Framework. Assume our data consists of instances x = ( x 1 , x 2 , ..., x n ) Assume data can be separated into two classes, positive and negative, by a linear decision surface .

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Reinforced Tabu Search (RTS) for Graph Colouring

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Linear Discriminant Functions

Linear Discriminant Functions. Wen-Hung Liao, 11/25/2008. Introduction: LDF. Assume we know the proper form of the discriminant functions , instead of the underlying probability densities. Use samples to estimate the parameters of the classifier.(statistical or non-statistical)

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Directional Derivatives in the Plane

Chapter 14. 14.5 Directional Derivatives and Gradient Vectors. Directional Derivatives in the Plane. The next map shows the contours on the area in New York. The tributary streams flow perpendicular to the contour.

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Survey of unconstrained optimization gradient based algorithms

Survey of unconstrained optimization gradient based algorithms. Unconstrained minimization Steepest descent vs. conjugate gradients Newton and quasi-Newton methods Matlab fminunc. Unconstrained local minimization. The necessity for one dimensional searches

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