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Jul
2
awarded  Curious
Mar
16
accepted Diagonalization of $M=ab^t+ba^t$
Mar
5
awarded  Yearling
Feb
8
asked Singular value of random matrix after linear transformation
Jan
13
comment Diagonalization of $M=ab^t+ba^t$
@yoirgos: thanks ! Can you conclude and give the values of $\lambda_1$ and $\lambda_2$ and the formula for the associated eigenvectors ?
Jan
13
asked Diagonalization of $M=ab^t+ba^t$
Jan
12
asked Diagonalization of a covariance matrix
Jan
16
revised What is the definition of front propagation?
added 57 characters in body
Jan
16
asked What is the definition of front propagation?
Oct
25
comment Approximation of stochastic differential equations
@TheBridge : thanks. Of course studying z_t and applying Gronwall Lemma is the classical strategy. But here the problem is that you cannot bound the Lipschitz coefficient because it is only assumed locally lipschitz...
Oct
24
asked Approximation of stochastic differential equations
Aug
14
awarded  Tumbleweed
Jul
8
accepted spectral norm of random matrix
Jul
6
awarded  Nice Question
May
2
awarded  Commentator
May
2
accepted Linear inhomogeneous PDE
May
2
comment Linear inhomogeneous PDE
Thnak you! I give you the answer.
May
1
comment Linear inhomogeneous PDE
Thank you very much. I think your value of $\lambda^*$ slightly overestimates the real one. This is maybe because $\bar{u}-u$ is not completely negligible as you mentioned. How could we improve your estimate ?
May
1
asked Linear inhomogeneous PDE
May
1
comment Eigenvalues of the 1D laplacian with mixed boundary conditions
@GiuseppeNegro Thank you! It confirms my first intuition then. So I guess we cannot solve $u=\Delta u$ with such B.C. using the usual separation of variable method... Because when doing so, we decompose the solution along the eigenvectors, and here without a discrete spectrum I do not know how to do it.