Memory efficient inverse noise covariance operators for litebird_sim#13
Merged
anand-avinash merged 9 commits intomainfrom Sep 5, 2025
Merged
Memory efficient inverse noise covariance operators for litebird_sim#13anand-avinash merged 9 commits intomainfrom
anand-avinash merged 9 commits intomainfrom
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…nted similarly to scipy.sparse.linalg.cg(), it causes the stopping criteria to become dynamic, depending on the norm of vector b. This change simplifies the convergence behavior
…cept a dict mapping block size to the noise property as block_input. This enables using a single instance of noise operator multiple times in the block list by reference if the block size and noise properties are same. This saves the memory a lot
… covariance blocks by reference
…rse covariance blocks by reference
…se covariance blocks by reference
…cg converges for inverse Toeplitz covariance
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This PR refactors the definition of inverse noise covariance operators in
litebird_simfor cases where the noise properties are identical across all observations and detectors.Previously, the implementation constructed a separate inverse covariance operator for each stationary chunk, storing the noise properties (power spectrum/covariance) independently for every chunk. With this update, if the noise properties are the same across stationary intervals, only one inverse covariance operator is constructed for each chunk size, and it is reused for all chunks of the same size.
The new implementation lowers the memory footprint significantly and allows faster initialization of the inverse noise covariance operator.