3 edition of **Newton acceleration of the Weiszfeld algorithm for minimizing the sum of Euclidean distances** found in the catalog.

Newton acceleration of the Weiszfeld algorithm for minimizing the sum of Euclidean distances

Yuying Li

- 63 Want to read
- 34 Currently reading

Published
**1995**
by Cornell Theory Center, Cornell University in Ithaca, N.Y
.

Written in English

**Edition Notes**

Statement | Yuying Li. |

Series | Technical report / Cornell Theory Center -- CTC95TR224., Technical report (Cornell Theory Center) -- 224. |

Contributions | Cornell Theory Center. |

The Physical Object | |
---|---|

Pagination | 21 p. : |

Number of Pages | 21 |

ID Numbers | |

Open Library | OL17010369M |

OCLC/WorldCa | 34328643 |

A Grid-Based Approximation Algorithm for the Minimum Weight Triangulation Problem Sharath Raghvendra y, Mari ette C. Wessels z Abstract Given a set of npoints on a plane, in the Minimum Weight Triangulation problem, we wish to nd a triangulation that minimizes the sum . Computational Acceleration of Projection Algorithms for the Linear Best Approximation Problem the Euclidean space. These algorithms employ diﬀerent iterative ap- [18] proposed a synthesis of Dykstra’s algorithm with Bregman distances and obtained a new algorithm that solves the BAP with Bregman projections. However, they established.

Using the Euclidean Algorithm The Decanting Problem is a liquid measuring problem that begins with two unmarked decanters with capacities a and b. Usually a and b are integers. The problem is to determine the smallest amount of liquid that can be measured and how such amount can be obtained, by a process of ﬁlling, pouring, and dumping. A Schrödinger Equation for the Fast Computation of Approximate Euclidean Distance Functions Karthik S. Gurumoorthy and Anand Rangarajan Dept. of Computer and Information Science and Engineering University of Florida, Gainesville, FL, USA Abstract. Computational techniques adapted from classical mechanics and usedCited by:

Constant Acceleration: Car Physics Cars provide examples for several areas in physics. This page uses the car pictured for a few simple examples to illustrate the chapters Constant acceleration, Weight and contact forces and Energy and power. It also has example problems posed to us about cars. We'll add more later. Acceleration, speed, time. Mathematical Function Optimization Using A Novel Algorithm Based On Newtonian Field Theory Todd Perry tion is maximizing or minimizing a given real function, for example, minimise energy consumption, maximize the proﬁt in p dimensional euclidean space are no new paradigm. In Particle Swarm Optimization (PSO) was Cited by: 1.

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A Newton Acceleration of the Weiszfeld Algorithm for Minimizing the Sum of Euclidean Distances Article (PDF Available) in Computational Optimization and Applications 10(3) · July A Cholesky factorization of a symmetric positive definite band matrix, typically with a small band width (e.g., a band width of two for a Euclidean location problem on a plane) is performed.

This new algorithm can be regarded as a Newton acceleration to the Weiszfeld Cited by: minimizing the sum of euclidean norms with box constraints I am a graduate student in computer science, making a thesis on uncertainty geometry.

During my thesis I came across the following optimi. Let f be a convex function bounded below with infimum f min attained. A bracket is an interval [L, U] containing f min. The Newton Bracketing (NB) method for minimizing f, introduced in [Levin and.

Jan 13, · Li Y () A Newton acceleration of the Weiszfeld algorithm for minimizing the sum of Euclidean distances. Comput Optim Appl – CrossRef Google Scholar Love RF, Dowling PD () A new bounding method for single facility location displacementdomesticity.com by: An Efficient Algorithm for Minimizing a Sum of Euclidean Norms with Applications by solving the Newton equation in linear time using Gaussian elimination on leaves of a tree, we present an.

Abstract. An interior point method is proposed for a general nonlinear (non- convex) minimization with linear inequality constraints. This method is a combination of the trust region idea for nonlinearity and affine scaling technique for displacementdomesticity.com by: 3.

Intuition on why the average minimizes the euclidean distance. Ask Question Asked 5 years, 11 months ago. $\begingroup$ It should be sum of squared euclidean distance, The insight is to interpret the sum of squared distances as an actual squared distance in a.

Faster Minimization of Linear Wirelength for Global Placement of the Weiszfeld Algorithm for Minimizing the Sum of Euclidean Distances regarded as a Newton acceleration to the Weiszfeld. Faster minimization of linear wirelength for global placement.

algorithm for minimizing a sum of Euclidean norms with non-convex constraints as a Newton acceleration to the Weiszfeld. Li Y () A Newton Acceleration of the Weiszfeld Algorithm forMinimizing the Sum of Euclidean Distances, Computational Optimization and Applications.

The book discusses block relaxation, alternating least squares, augmentation, and majorization algorithms to minimize loss functions, with applications in statistics, multivariate analysis, and multidimensional scaling. We're upgrading the ACM DL, and would like your input.

Please sign up to review new features, functionality and page displacementdomesticity.com by: Given two sets of d-dimensional points. How can I most efficiently compute the pairwise squared euclidean distance matrix in Matlab. Notation: Set one is given by a (numA,d)-matrix A and set two is.

Statistica Sinica 5 (), A QUASI-NEWTON CCELERA TION OF THE EM ALGORITHM Kenneth Lange University of This acceleration seeks to steer the EM algorithm gradually to w ard the Newton-Raphson algorithm, whic Gauss-Newton algorithm, one can retain rst sum exactly as giv en in (1) and attempt to appro ximate the second sum.

This. Newton Revisited: An excursion in Euclidean geometry Greg Markowsky October 26, Abstract This paper discusses the relationship between Kepler’s Laws and Euclidean geometry.

Many of the theorems are from Principia by Isaac Newton, but a more modern manner of presentation is adopted. 1 Introduction. Thus, the quasi-Newton method is feasible for high-di-mensional problems and potentially faster than SQUAREM if we take q>1.

It takes two ordinary iterates to generate a secant condition and quasi-Newton update. If the quasi-Newton update fails to send the objective function in the right direction, then with an ascent or descent algorithm one. Acceleration, inexact Newton, and Nonlinear Krylov subspace methods Yousef Saad Department of Computer Science and Newton-type method, acceleration ä Acceleration techniques ä Anderson acceleration ä Epsilon algorithm and related topics.

Why is Newton's method not widely used in machine learning. Ask Question That can be faster when the second derivative is known and easy to compute (the Newton-Raphson algorithm is used in logistic regression). The author claims he used a simple version of Newton's method so as to have an apples to apples comparison between Euclidean.

Dec 12, · The squared iterative methods (SQUAREM) recently proposed by Varadhan and Roland constitute one notable exception.

This paper presents a new quasi-Newton acceleration scheme that requires only modest increments in computation per iteration and overall storage and rivals or surpasses the performance of SQUAREM on several representative test Cited by:. Abstract. A sequential algorithm is presented for computing the Euclidean distance transform of a k-dimensional binary image in time linear in the total number of displacementdomesticity.com algorithm may be of practical value since it is relatively simple and easy to implement and it is relatively fast (not only does it run in linear time but the time constant is small).Cited by: The geometric median of a discrete set of sample points in a Euclidean space is the point minimizing the sum of distances to the sample points.

This generalizes the median, which has the property of minimizing the sum of distances for one-dimensional data, and provides a central tendency in higher dimensions, it is also known as the 1-median, spatial median, Euclidean minisum point, or.Difference between Gauss-Newton method and quasi-Newton method for optimization.

Ask Question Asked 4 years, but they are sufficiently good that you can use them to obtain good search directions in the optimization algorithm. There are many quasi-Newton methods, of which the most popular is probably BFGS (Broyden-Fletcher-Goldfarb-Shanno.).