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Philipp Trunschke's user avatar
Philipp Trunschke's user avatar
Philipp Trunschke
  • Member for 9 years, 5 months
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accepted
Change of the smallest positive eigenvalue after a rank-one update
revised
Change of the smallest positive eigenvalue after a rank-one update
added 60 characters in body
asked
Change of the smallest positive eigenvalue after a rank-one update
comment
sparsity of QR decomposition
Could you please elaborate?
accepted
Can a weighted $\ell^p$ norm be bounded by an unweighted $\ell^q$ norm?
revised
Can a weighted $\ell^p$ norm be bounded by an unweighted $\ell^q$ norm?
Clarify the question
revised
Can a weighted $\ell^p$ norm be bounded by an unweighted $\ell^q$ norm?
Clarify the second statement.
awarded
 Teacher
answered
Can a weighted $\ell^p$ norm be bounded by an unweighted $\ell^q$ norm?
revised
Can a weighted $\ell^p$ norm be bounded by an unweighted $\ell^q$ norm?
Answer my own question
revised
Can a weighted $\ell^p$ norm be bounded by an unweighted $\ell^q$ norm?
Answer parts of my question.
revised
Can a weighted $\ell^p$ norm be bounded by an unweighted $\ell^q$ norm?
Add another related question.
revised
Can a weighted $\ell^p$ norm be bounded by an unweighted $\ell^q$ norm?
Explain further what I mean by a non-trivial sequence.
comment
Can a weighted $\ell^p$ norm be bounded by an unweighted $\ell^q$ norm?
I don’t understand how. Since $p>q$, it always holds that $\Vert v\Vert_{\ell^p_1} = \Vert v\Vert_{\ell^p} \le \Vert v\Vert_{\ell^q}$.
revised
Can a weighted $\ell^p$ norm be bounded by an unweighted $\ell^q$ norm?
Phrased the last question more clearly.
asked
Can a weighted $\ell^p$ norm be bounded by an unweighted $\ell^q$ norm?
accepted
Was a quotient of two norms considered as a constraint to a convex optimization problem before?
awarded
 Editor
revised
Was a quotient of two norms considered as a constraint to a convex optimization problem before?
added 26 characters in body
comment
Was a quotient of two norms considered as a constraint to a convex optimization problem before?
If $g$ is an empirical risk, then cross-validation seems to be a logical choice. For general $g$, however, it can not be used. A simple strategy would obviously be to solve the problem for different values of $M$. Increasing $M$ when the constraint is active and decreasing $M$ when the constraint is not active. Do you think there is a better way than a bisection method?
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