John Salvatier
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 Feb 9 awarded Notable Question Dec 14 awarded Popular Question Jul 2 awarded Curious Jun 13 awarded Popular Question Nov 15 awarded Popular Question Feb 4 accepted solve $y = (A+B^{-1})x$ for $x$ Jan 26 comment solve $y = (A+B^{-1})x$ for $x$ I can just calculate $solve(BA+I, By)$, no? Jan 26 comment solve $y = (A+B^{-1})x$ for $x$ Or is there some reason that doesn't work? Jan 26 comment solve $y = (A+B^{-1})x$ for $x$ I think for my purposes, I can just solve $$(BA+I)x = By$$ $$x = (BA + I)^{-1}By$$ Give that as an answer, and I'll accept. Jan 26 revised solve $y = (A+B^{-1})x$ for $x$ added 40 characters in body Jan 26 comment solve $y = (A+B^{-1})x$ for $x$ Yes, sorry, that is indeed what I meant. Jan 26 comment solve $y = (A+B^{-1})x$ for $x$ That sounds like just the kind of thing I need, but I don't know what that is. Can you elaborate? Maybe I'm just dense. Jan 26 revised solve $y = (A+B^{-1})x$ for $x$ added 61 characters in body; edited title Jan 26 asked solve $y = (A+B^{-1})x$ for $x$ Jan 14 accepted 1st-order linear ODE with tridiagonal matrix. Efficient solutions? Jan 10 asked 1st-order linear ODE with tridiagonal matrix. Efficient solutions? Jan 10 comment Is there general formula for the exponential of a tridiagonal matrix? I'm looking for a way to compute exp(At)*x_0 cheaply when A's a symmetric tridiagonal matrix. I think I may just have to eigen-decompose A and do it that way. Luckily I only have to decompose A once, and then it's O(n**2), which I guess is okay. Since you should be able to compute Ax_0 in O(n) steps since its tridiagonal, I was hoping for something better, but maybe that's not possible. Jan 10 comment Is there general formula for the exponential of a tridiagonal matrix? Did you ever find a solution to your problem? Jul 10 comment Random samples from a normal distribution without explicitly constructing a covariance matrix Yes, but the tricky part is that I need it with the covariance matrix C^-1 not C Jul 9 asked Random samples from a normal distribution without explicitly constructing a covariance matrix