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32m
comment Combinatorial Convex Optimization: Russian paper
You could try scholar.reddit.com.
2h
comment Some clarification on nonlinear PDEs
There must be some mistake in the way you are using Strauss's definition of a linear operator. Can you give more details about that? If $L$ is the operator defined by $L(u) = u_x + u u_y$, it's not necessarily true that $L(u_1 + u_2) = L(u_1) + L(u_2)$, or that $L(c u) = c L(u)$ for all scalars $c$.
1d
comment How do mathematician make sense of “outcome” and “events” in probability?
Have you learned about the axioms for a probability space? You can model physical situations by introducing an appropriate probability space, and then reasoning about the probability space.
1d
comment convex optimization?
But $A$ needs to be a column vector in order for the output of $f$ to be a scalar.
1d
comment What allows us divide/multiply dx in calculus?
@janesmith Note that the function I'm integrating is $ f (g (x)) g'(x) $ , not $ f '(g (x)) g'(x) $, so I think my formula was correct as written.
1d
revised about scaling property of proximal operator
deleted 14 characters in body
1d
answered about scaling property of proximal operator
2d
comment Do I have the correct mental map for adjoint operators for inner product spaces?
I prefer to draw $N(A)$ and $R(A^*)$ as one-dimensional orthogonal subspaces on the left, and draw $R(A)$ and $N(A^*)$ as one-dimensional orthogonal subspaces on the right.
2d
answered What allows us divide/multiply dx in calculus?
Aug
24
comment How do you find redundant constraints for a feasible region?
Vandenberghe's 236c notes -- specifically the chapter Analytic centering cutting- plane method have some useful material on pruning constraints.
Aug
24
comment Do mathematicians prefer eigenvectors with purely integer entries?
I think the answer is no, there is (usually, at least) no reason to prefer eigenvectors with purely integer entries.
Aug
23
comment Why is it called a “multiset”?
"One of the miseries of life is that everybody names things a little bit wrong, and so it makes everything a little harder to understand." -- Richard Feynman (said at 4:56 in his computer heuristics lecture)
Aug
19
comment proximal operator of weighted L1 norm
Assuming that $\| CX \|_1$ is the sum of the absolute values of $CX$, and that $X \geq 0$ means each component of $X$ is nonnegative, your problem separates into $n$ independent subproblems, one for each column of $X$. So there is no problem with $X$ being a matrix.
Aug
19
comment proximal operator of weighted L1 norm
Since $C X$ is an $m \times n$ matrix, what do you mean by the $1$-norm of $CX$? I suspect you don't mean the matrix norm induced by the vector $1$-norm. Is $\| CX \|_1$ just the sum of the absolute values of the entries of $CX$?
Aug
19
comment proximal operator of weighted L1 norm
Are you assuming that $C$ is diagonal?
Aug
19
comment Convex Optimization Closed Form Solution
@dohmatob Our comments here might not be useful for other people, so if you'd like we can delete them to clean things up.
Aug
19
comment What is the intuition behind the cross product?
I assume you're not satisfied with the explanation that in physics the cross product is useful for computing the flux through a surface. Mathematically, the cross product is an alternating bilinear function of its inputs, which is such a simple thing that it's almost guaranteed to be relevant -- but hopefully other people will provide a more enlightening explanation. Note that $\text{det}(a,b,c) = a \cdot (b \times c)$.
Aug
19
comment How to download alyx startup kit?
This question is not on topic here, but you might get an answer at tex.stackexchange if you describe specifically and clearly what exactly you are stuck on.
Aug
17
answered Understanding the differential $dx$ when doing $u$-substitution
Aug
14
comment differentiation of 1-norm of a vector
These functions are non differrentiable, so what do you mean by differentiating them?