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CORR
2010
Springer
70views Education» more  CORR 2010»
13 years 10 months ago
Structured sparsity-inducing norms through submodular functions
Sparse methods for supervised learning aim at finding good linear predictors from as few variables as possible, i.e., with small cardinality of their supports. This combinatorial ...
Francis Bach
DISOPT
2008
113views more  DISOPT 2008»
13 years 10 months ago
The inverse 1-median problem on a cycle
Abstract. Let the graph G = (V, E) be a cycle with n + 1 vertices, nonnegative vertex weights and positive edge lengths. The inverse 1-median problem on a cycle consists in changin...
Rainer E. Burkard, Carmen Pleschiutschnig, Jianzho...
CPC
2006
110views more  CPC 2006»
13 years 10 months ago
Solving Sparse Random Instances of Max Cut and Max 2-CSP in Linear Expected Time
Abstract. We show that a maximum cut of a random graph below the giantcomponent threshold can be found in linear space and linear expected time by a simple algorithm. In fact, the ...
Alexander D. Scott, Gregory B. Sorkin
EOR
2006
59views more  EOR 2006»
13 years 10 months ago
Sensitivity analysis in linear optimization: Invariant support set intervals
Sensitivity analysis is one of the most interesting and preoccupying areas in optimization. Many attempts are made to investigate the problem's behavior when the input data c...
Alireza Ghaffari Hadigheh, Tamás Terlaky
CORR
2007
Springer
88views Education» more  CORR 2007»
13 years 10 months ago
Design and Fabrication of a Micro Electrostatic Vibration-to-Electricity Energy Converter
This paper presents a micro electrostatic vibration-toelectricity energy converter. For the 3.3 V supply voltage and 1cm2 chip area constraints, optimal design parameters were fou...
Yi Chiu, Chiung-Ting Kuo, Yu-Shan Chu