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Optimization with convex constraints and convex objectives; notions related to convex optimization such as sub-gradients, normal cones, separating hyperplanes
2
votes
Accepted
When are infimal convolutions contractions?
Here are just some thoughts. I think it is a matter of curvature, so let us assume that $\varphi$ and $\psi$ are smooth. Then, $y(x)$ solves the optimality condition
$$
\psi'(y(x)) = \varphi'(x - y(x) …
1
vote
Optimal transport: the existence of an optimal pair of $c$-conjugate functions
I was also struggling with Exercise 2.36... I think that I am now able to solve it, although it seems that it is more difficult than it appears...
The key seems to be the following theorem.
Theorem: …
0
votes
Optimality condition for strongly convex function under sparsity constraint
I don't think that this is true. Let us take $p = 2$, $s = 1$ and $f(x) = \frac12 \|x - (1,1)\|^2$. Then, $\theta_0 = (1,0)$ is a minimizer, but with $\theta = (0,1)$ we get
$$
\nabla f(\theta_0)^\top …
1
vote
Is a Lipschitz continuous gradient equivalent to this condition?
Yes, the converse is also true. This follows from the answer in https://math.stackexchange.com/questions/4227159/characterization-of-lipschitz-derivative.
In fact, your condition yields
$$
| (\nabla f …
6
votes
Accepted
Are the polyhedral cones the only examples of cones that remains closed when they are added ...
The radial cone of $C$ is defined via
$$
\mathcal R_C(x) := \bigcup_{\lambda > 0} \lambda ( C - x)$$
for all $x \in C$
and we can show
$$
\mathcal R_C(x) = C + \operatorname{span}(x),
$$
since $C$ is …
3
votes
Accepted
Subgradient in a predual under weak* continuity
Finally, I was able to cook up a counterexample. We choose $X = c_0$ (zero sequences equipped with supremum norm). Thus, the dual spaces are (isometric to) $X^* = \ell^1$ and $X^{**} = \ell^\infty$.
W …
1
vote
Accepted
When is a convex function continuous on its domain?
I don't think that this is true.
Let us take
$$
C := \{ x \in \mathbb R^2 \mid x_1^2 \le x_2 \le 1\}$$
and
$$
f(x) = \frac{x_1^2}{x_2}
$$
for $x \in C \setminus \{0\}$, $f(0,0) = 0$.
This function is …
3
votes
Accepted
How do you call a linear programming problem when the solution should be "constrained" to a ...
If you are willing to replace $a_i > 0$ by $a_i \ge 0$,
then this becomes a quadratic program. Indeed,
it can be formulated as
\begin{align*}
\text{Minimize}\quad & \frac12 a^\top Q a + q^\top a, \\
…
1
vote
Hardness of concave minimization problem
If your problem has a solution $x^* \ne 0$, then $0$ is also a solution. Indeed, consider the function
$$\varphi(t) = c(t \, x^*) - k\cdot (t \, x^*).$$
Since $x^*$ is a solution, we have
$$\varphi(0) …
1
vote
Second order necessary and sufficient conditions for convex nonsmooth optimization
For convex optimization problems, you do not need second-order conditions, because already the optimality conditions of first order characterize global optimality.
1
vote
Necessary conditions for optimality in Banach spaces
Let us assume that your $f$ is at least directionally differentiable at $x_0$ and that the directional derivative depends continuously on the direction (this may be satisfied under rather general assu …
6
votes
Accepted
Interior of a dual cone
The bad news is that many closed convex cones in infinite dimensions have empty interior, e.g., the natural cone of positive functions in $L^p(\Omega)$ for all $\Omega \subset \mathbb{R}^n$; or in $W^ …