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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