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Randomized greedy methods for weak submodular sensor selection with robustness considerations☆
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AI
DOI：10.1016/j.automatica.2024.111984.png)
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Abstract
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We study a pair of budget- and performance-constrained weak submodular maximization problems. For computational efficiency, we explore the use of stochastic greedy algorithms which limit the search space via random sampling instead of the standard greedy procedure which explores the entire feasible search space. We propose a pair of stochastic greedy algorithms, namely, MODIFIED RANDOMIZED GREEDY (MRG) and DUAL RANDOMIZED GREEDY (DRG) to approximately solve the budgetand performance-constrained problems, respectively. For both algorithms, we derive approximation guarantees that hold with high probability. We then examine the use of DRG in robust optimization problems wherein the objective is to maximize the worst-case of a number of weak submodular objectives and propose the RANDOMIZED WEAK SUBMODULAR SATURATION ALGORITHM (RANDOM-WSSA). We further derive a high-probability guarantee for when RANDOM-WSSA successfully constructs a robust solution. Finally, we showcase the effectiveness of these algorithms in a variety of relevant uses within the context of Earth-observing LEO constellations which estimate atmospheric weather conditions and provide Earth coverage. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and
Keywords:
Aerospace
Decision making and autonomy
Sensor data fusion
Distributed optimization for large-scale
systems
Large scale optimization problems
Modeling and decision making in complex
Probabilistic robustness
Robustness analysis
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W
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Cited Papers
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