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

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.

References in corpus (4)

• Convex Optimization for Big Data
• Determinant-based Fast Greedy Sensor Selection Algorithm
• Fast Greedy Optimization of Sensor Selection in Measurement with Correlated Noise
• Effect of Objective Function on Data-Driven Greedy Sparse Sensor Optimization

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• Determinant-based Fast Greedy Sensor Selection Algorithm
• Fast Greedy Optimization of Sensor Selection in Measurement with Correlated Noise
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• Nondominated-Solution-based Multi-objective Greedy Sensor Selection for Optimal Design of Experiments
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• Optimization of Sparse Sensor Placement for Estimation of Wind Direction and Surface Pressure Distribution Using Time-Averaged Pressure-Sensitive Paint Data on Automobile Model
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• Randomized Group-Greedy Method for Large-Scale Sensor Selection Problems
• Observation Site Selection for Physical Model Parameter Estimation toward Process-Driven Seismic Wavefield Reconstruction
• Fast Data-driven Greedy Sensor Selection for Ridge Regression
• Gathering and Exploiting Higher-Order Information when Training Large Structured Models