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 ...
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...
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 ...
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...
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...