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SIAMSC
2008
147views more  SIAMSC 2008»
13 years 8 months ago
Global and Finite Termination of a Two-Phase Augmented Lagrangian Filter Method for General Quadratic Programs
We present a two-phase algorithm for solving large-scale quadratic programs (QPs). In the first phase, gradient-projection iterations approximately minimize an augmented Lagrangian...
Michael P. Friedlander, Sven Leyffer
JACM
2007
79views more  JACM 2007»
13 years 8 months ago
A new look at survey propagation and its generalizations
This article provides a new conceptual perspective on survey propagation, which is an iterative algorithm recently introduced by the statistical physics community that is very effe...
Elitza N. Maneva, Elchanan Mossel, Martin J. Wainw...
JOTA
2011
98views more  JOTA 2011»
13 years 3 months ago
Generic Stability and Essential Components of Generalized KKM Points and Applications
We propose the definition of T-KKM points and consider generic stability of T-KKM mappings and essential components of sets of T-KKM points. As applications, using a unified appr...
Phan Quoc Khanh, Nguyen Hong Quan
CAD
2007
Springer
13 years 8 months ago
Computing general geometric structures on surfaces using Ricci flow
Systematically generalizing planar geometric algorithms to manifold domains is of fundamental importance in computer aided design field. This paper proposes a novel theoretic fra...
Miao Jin, Feng Luo 0002, Xianfeng David Gu
ICPR
2000
IEEE
14 years 9 months ago
General Bias/Variance Decomposition with Target Independent Variance of Error Functions Derived from the Exponential Family of D
An important theoretical tool in machine learning is the bias/variance decomposition of the generalization error. It was introduced for the mean square error in [3]. The bias/vari...
Jakob Vogdrup Hansen, Tom Heskes