'Quasi newton' diaporamas de présentation

Optimization

Optimization. Issues. What is optimization? What real life situations give rise to optimization problems? When is it easy to optimize? What are we trying to optimize? What can cause problems when we try to optimize? What methods can we use to optimize?. One-Dimensional Minimization.

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Qualifier Exam in HPC

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Chapter 5

Chapter 5 One Dimensional Search. Chapter 5. Unidimensional Search. If have a search direction, want to minimize in that direction by numerical methods. Chapter 5. Search Methods in General

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A Method for Determining Turbine Airfoil Geometry Parameters from a Set of Coordinates

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A Method for Determining Turbine Airfoil Geometry Parameters from a Set of Coordinates

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Zeroing in on the Implicit Function Theorem

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Zeroing in on the Implicit Function Theorem

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Toward an automatic parallel tool for solving systems of nonlinear equations

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Least Squares example

Least Squares example. There are 3 mountains u,y,z that from one site have been measured as 2474 ft., 3882 ft., and 4834 ft.. But from u, y looks 1422 ft. taller and the z looks 2354 ft. taller , and from y, z looks 950 ft. taller. Set up the overdetermined system. 2474 3882 4834 1422

352 views • 21 slides

Least Squares example

Least Squares example. There are 3 mountains u,y,z that from one site have been measured as 2474 ft., 3882 ft., and 4834 ft.. But from u, y looks 1422 ft. taller and the z looks 2354 ft. taller , and from y, z looks 950 ft. taller. Set up the overdetermined system. 2474 3882 4834 1422

279 views • 21 slides

Unconstrained Optimization

Unconstrained Optimization. Rong Jin. Logistic Regression. The optimization problem is to find weights w and b that maximizes the above log-likelihood. How to do it efficiently ?. Gradient Ascent. Compute the gradient Increase weights w and threshold b in the gradient direction.

1.23k views • 16 slides

Unconstrained optimization

472 views • 12 slides