 The first mitigation method is similar to a sparse sample of the original matrix, removing components that are not considered valuable.However, if the solution or detection process is near the noise level, truncating may remove components that influence the desired solution.The second mitigation extends the eigenvalue so that lower values have much less influence over inversion, but do still contribute, such that solutions near the noise will still be found.