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methods: Optimization Algorithms

This module provides various algorithms to optimize the sensor placements or paths. Use the get_methods method to retrieve an optimization method class by its string name.

  • ContinuousSGP: This method directly optimizes the inducing points of the AugmentedSGPR model to maximize the Evidence Lower Bound (ELBO). This is the main SGP-based optimization approach proposed in the papers associated with this library.

  • GreedySGP and GreedyObjective: These implement greedy algorithms for sensor placement. GreedySGP iteratively selects inducing points to maximize the SGP's ELBO, while GreedyObjective uses a more general objective function like Mutual Information.

  • BayesianOpt: This method uses Bayesian Optimization, a powerful black-box optimization algorithm, to find the best sensor locations by maximizing a general objective function.

  • CMA: This method uses Covariance Matrix Adaptation Evolution Strategy (CMA-ES), a powerful black-box optimization algorithm, to find the best sensor locations by maximizing a general objective function.