Prokop Hapala
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 Apr 18 accepted permutation symmetric hash function Apr 18 comment permutation symmetric hash function heh, on first look I would think that it would not be very good hash (with respect to 1. avalanche 2. statistical properties ), but actually why I don't see any specific problem why not. Perhaps you are right. Mar 24 comment B-splines locally controlled yes, if you sample an interval of $x$ by finer grid than basis function $\phi(x)$ has smaller extend (are more contracted, perhabs I should write $\phi_i( (x/L) - i)$ where $L$ is the sampling step size ) so at any point $x$ just 4 of them are non-zero. But because they are more contracted, they are sharper, so you can reproduce finer details of your function which you want to approximate. Mar 23 awarded Teacher Mar 23 answered B-splines locally controlled Mar 23 asked Sphere overlap with cubic grid in R^N; minimal number of grid cells Mar 11 comment permutation symmetric hash function That is certainly a solution. But I was thinking that by design of hash function It would be possible to avoid this, I consider sorting is slower and more complex algorithm than computation of hash. Mar 11 asked permutation symmetric hash function Dec 2 accepted optimization with constrained coefitients of linear combination Dec 2 comment optimization with constrained coefitients of linear combination aha, sorry, I didn't realized that you did this transfromation $c^T(b+Af) = (A^T c)^T f + const.$ ... stupid me ... now it is clear, thanks Dec 2 comment optimization with constrained coefitients of linear combination dohmatob, first, thanks for the effort to read it. But I think you don't get it exactly right. If I use your formalism, my cost function is $r^T c$ (where $c$ is fixed) and $r:= A^T f + b$ (with fixed $A$ and $b$) is some transformation from $m$-dimensional space of $f$-s to $n$-dimensional space on which cost function is defined ) ... the fact that constrains are on $f$ ( instead of $c$ ) makes it ( I think ) a bit different from LPs examples I found on internet. Second, I'm not sure how offsetting the constrains by tiny $\delta,\gamma$ change the situation, but I guess can easily do that. Dec 1 revised optimization with constrained coefitients of linear combination deleted 17 characters in body Dec 1 asked optimization with constrained coefitients of linear combination Jun 16 revised Derivative of angular function by cartesian coordinates using Legendre polynomials? edited title Jun 16 revised Derivative of angular function by cartesian coordinates using Legendre polynomials? make more clean discussion of r vs r^2 Jun 16 asked Derivative of angular function by cartesian coordinates using Legendre polynomials? May 13 comment principal components of mutual covariance marix Yes, I know SVD, my question is if it still works (either with SVD or with eingenvalue decompostion ) even in case when the cross correlation matrix is not self adjoint ( which I guess is not when $X$ and $Y$ are arbitrary matrixes ), so I guess it is also not necesarlily positive definite. But maybe there is some way how to make it positive definite and still capture the information about correlations like using SVD of $M^T M$ instead of $M$ ?? May 12 asked principal components of mutual covariance marix Apr 25 accepted Time dependence of velocity from position dependece of velocity Apr 25 awarded Supporter