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

Taku Nonomura, Shunsuke Ono, Kumi Nakai, Yuji Saito

Research output: Contribution to journalArticlepeer-review

Abstract

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

Original languageEnglish
JournalIEEE Signal Processing Letters
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Computational efficiency
  • Convergence
  • Convex sensor selection problem
  • Data-driven sensor selection
  • Heuristic algorithms
  • Linear programming
  • Newton method
  • Randomized subspace Newton algorithm
  • Signal processing algorithms
  • Sparse matrices

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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