Randomized Subspace Newton Convex Method Applied to Data-Driven Sensor Selection Problem

The randomized subspace Newton convex methods for the sensor selection problem are proposed. algorithm is straightforwardly applied to formulation, and customized method in which part of update variables selected be present best candidates also considered. In converged solution, almost same results obtained by original randomized-subspace-Newton methods. As expected, require more computational steps while they reduce total amount time because one step significantly reduced cubic ratio numbers randomly updating all variables. shows superior performance straightforward implementation terms quality sensors time.