The spectral gap refers to the difference between the largest eigenvalue and the second largest eigenvalue of an operator or matrix. It is a crucial concept in understanding stability and convergence in various mathematical contexts, particularly in relation to truncated singular value decomposition (TSVD). A larger spectral gap often indicates better performance and accuracy in approximations made by TSVD, as it signifies a clear separation between dominant and less significant singular values.
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