Sciweavers

SIAMSC
2010
104views more  SIAMSC 2010»
13 years 2 months ago
A New Sobolev Gradient Method for Direct Minimization of the Gross--Pitaevskii Energy with Rotation
Abstract. In this paper we improve traditional steepest descent methods for the direct minimization of the Gross-Pitaevskii (GP) energy with rotation at two levels. We first defi...
Ionut Danaila, Parimah Kazemi
JCC
2002
74views more  JCC 2002»
13 years 4 months ago
Improved RGF method to find saddle points
: The predictor-corrector method for following a reduced gradient (RGF) to determine saddle points [Quapp, W. et al., J Comput Chem 1998, 19, 1087] is further accelerated by a modi...
Michael Hirsch, Wolfgang Quapp
SIAMJO
2000
67views more  SIAMJO 2000»
13 years 4 months ago
Gradient Convergence in Gradient methods with Errors
We consider the gradient method xt+1 = xt + t(st + wt), where st is a descent direction of a function f : n and wt is a deterministic or stochastic error. We assume that f is Lip...
Dimitri P. Bertsekas, John N. Tsitsiklis
PAMI
2000
133views more  PAMI 2000»
13 years 4 months ago
Mode-Finding for Mixtures of Gaussian Distributions
I consider the problem of finding all the modes of a mixture of multivariate Gaussian distributions, which has applications in clustering and regression. I derive exact formulas f...
Miguel Á. Carreira-Perpiñán
TCAD
2008
68views more  TCAD 2008»
13 years 4 months ago
Highly Efficient Gradient Computation for Density-Constrained Analytical Placement
Abstract--Recent analytical global placers use density constraints to approximate nonoverlap constraints, and these show very successful results. This paper unifies a wide range of...
Jason Cong, Guojie Luo, Eric Radke
IJCV
2007
144views more  IJCV 2007»
13 years 4 months ago
Generalized Gradients: Priors on Minimization Flows
This paper tackles an important aspect of the variational problem underlying active contours: optimization by gradient flows. Classically, the definition of a gradient depends d...
Guillaume Charpiat, Pierre Maurel, Jean-Philippe P...
SIAMJO
2008
212views more  SIAMJO 2008»
13 years 4 months ago
Convergence Rate of an Optimization Algorithm for Minimizing Quadratic Functions with Separable Convex Constraints
A new active set algorithm for minimizing quadratic functions with separable convex constraints is proposed by combining the conjugate gradient method with the projected gradient. ...
Radek Kucera
MP
2006
80views more  MP 2006»
13 years 4 months ago
Minimizing Polynomials via Sum of Squares over the Gradient Ideal
A method is proposed for finding the global minimum of a multivariate polynomial via sum of squares (SOS) relaxation over its gradient variety. That variety consists of all points ...
Jiawang Nie, James Demmel, Bernd Sturmfels