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ALT
2004
Springer
14 years 4 months ago
Convergence of a Generalized Gradient Selection Approach for the Decomposition Method
The decomposition method is currently one of the major methods for solving the convex quadratic optimization problems being associated with support vector machines. For a special c...
Nikolas List
SIAMJO
2010
130views more  SIAMJO 2010»
13 years 2 months ago
A Randomized Cutting Plane Method with Probabilistic Geometric Convergence
Abstract. We propose a randomized method for general convex optimization problems; namely, the minimization of a linear function over a convex body. The idea is to generate N rando...
Fabrizio Dabbene, P. S. Shcherbakov, Boris T. Poly...
APPROX
2004
Springer
179views Algorithms» more  APPROX 2004»
14 years 24 days ago
Maximum Weight Independent Sets and Matchings in Sparse Random Graphs. Exact Results Using the Local Weak Convergence Method
ABSTRACT: Let G(n, c/n) and Gr(n) be an n-node sparse random graph and a sparse random rregular graph, respectively, and let I(n, r) and I(n, c) be the sizes of the largest indepen...
David Gamarnik, Tomasz Nowicki, Grzegorz Swirszcz
TNN
2010
176views Management» more  TNN 2010»
13 years 2 months ago
On the weight convergence of Elman networks
Abstract--An Elman network (EN) can be viewed as a feedforward (FF) neural network with an additional set of inputs from the context layer (feedback from the hidden layer). Therefo...
Qing Song
ICML
2001
IEEE
14 years 8 months ago
Convergence of Gradient Dynamics with a Variable Learning Rate
As multiagent environments become more prevalent we need to understand how this changes the agent-based paradigm. One aspect that is heavily affected by the presence of multiple a...
Michael H. Bowling, Manuela M. Veloso