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CORR
2011
Springer
157views Education» more  CORR 2011»
12 years 11 months ago
Large-Scale Convex Minimization with a Low-Rank Constraint
We address the problem of minimizing a convex function over the space of large matrices with low rank. While this optimization problem is hard in general, we propose an efficient...
Shai Shalev-Shwartz, Alon Gonen, Ohad Shamir
WABI
2005
Springer
124views Bioinformatics» more  WABI 2005»
14 years 1 months ago
Reconstructing Metabolic Networks Using Interval Analysis
Recently, there has been growing interest in the modelling and simulation of biological systems. Such systems are often modelled in terms of coupled ordinary differential equation...
Warwick Tucker, Vincent Moulton
SIAMSC
2010
155views more  SIAMSC 2010»
13 years 2 months ago
Quasi-Newton Methods on Grassmannians and Multilinear Approximations of Tensors
In this paper we proposed quasi-Newton and limited memory quasi-Newton methods for objective functions defined on Grassmannians or a product of Grassmannians. Specifically we defin...
Berkant Savas, Lek-Heng Lim
ESA
2008
Springer
108views Algorithms» more  ESA 2008»
13 years 9 months ago
Two-Stage Robust Network Design with Exponential Scenarios
Abstract. We study two-stage robust variants of combinatorial optimization problems like Steiner tree, Steiner forest, and uncapacitated facility location. The robust optimization ...
Rohit Khandekar, Guy Kortsarz, Vahab S. Mirrokni, ...
NIPS
2008
13 years 9 months ago
An interior-point stochastic approximation method and an L1-regularized delta rule
The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning. Despite its farreaching application, there is almost n...
Peter Carbonetto, Mark Schmidt, Nando de Freitas