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MP
2008
101views more  MP 2008»
13 years 4 months ago
Accelerating the cubic regularization of Newton's method on convex problems
In this paper we propose an accelerated version of the cubic regularization of Newton's method [6]. The original version, used for minimizing a convex function with Lipschitz...
Yu. Nesterov
MP
2006
134views more  MP 2006»
13 years 4 months ago
Cubic regularization of Newton method and its global performance
In this paper, we provide theoretical analysis for a cubic regularization of Newton method as applied to unconstrained minimization problem. For this scheme, we prove general local...
Yurii Nesterov, Boris T. Polyak
JMLR
2010
143views more  JMLR 2010»
13 years 3 months ago
A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning
We extend the well-known BFGS quasi-Newton method and its memory-limited variant LBFGS to the optimization of nonsmooth convex objectives. This is done in a rigorous fashion by ge...
Jin Yu, S. V. N. Vishwanathan, Simon Günter, ...
ICDM
2009
IEEE
149views Data Mining» more  ICDM 2009»
13 years 11 months ago
Accelerated Gradient Method for Multi-task Sparse Learning Problem
—Many real world learning problems can be recast as multi-task learning problems which utilize correlations among different tasks to obtain better generalization performance than...
Xi Chen, Weike Pan, James T. Kwok, Jaime G. Carbon...
JMLR
2010
161views more  JMLR 2010»
12 years 11 months ago
Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization
We consider regularized stochastic learning and online optimization problems, where the objective function is the sum of two convex terms: one is the loss function of the learning...
Lin Xiao