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6
votes
Minimum of squared sum minus sum of squares
If $z_k = x_k y_k$, the quantity you're looking at is
$$ y^T D Q D y = \left(\sum_k z_k\right)^2 - \sum_k z_k^2$$
where $Q$ is the $n \times n$ symmetric matrix with diagonal terms $0$ and off-diagon …
6
votes
Accepted
Is there a non-convex function with non-decreasing average rate of change?
The standard example of a non-convex but midpoint-convex function is additive:
given a basis $B$ of $\mathbb R$ over the rationals $\mathbb Q$, choose one member $\alpha$ of $B$ and
if $x = \sum_{\bet …
2
votes
Extreme points of an intersection of convex set with countably many linear spaces
The last paragraph is wrong. Consider the case $m=1$. $\tilde{M}$ is the intersection of $K$ with one hyperplane. In general this will not contain any extreme point of $K$, so its extreme points wi …
1
vote
Euclidean projection onto certain convex set
Assume wlog $\|c\|=1$. $C = \{0\}$ if $\alpha > 1$, while it is the ray
$(-\infty, 0] c$ if $\alpha = 1$. So let's suppose $0 < \alpha < 1$.
If $y = t c + w$ where $t \in \mathbb R$ and $\langle c, …
1
vote
Zero lambda, zero constraint in the complementary slackness condition of the Kuhn-Tucker pro...
This can occur even in linear programming, in the presence of degeneracy. At an optimal basic solution, the slack variable for some binding constraint may be basic (but with value $0$ since it is bin …
2
votes
Accepted
How to find extreme points of a set related to Minkowski's Theorem?
I'll assume $m, n \ge 2$.
I claim that
$$\Lambda = \left\{\lambda \in \mathbb R^m: \; \sum_{i=1}^m \lambda_i = 1,\; 0 \le \lambda_i \le \frac{1}{2} \ \text{for all}\ i\right \}$$
The necessity o …
4
votes
Precompactness of a sequence of convex functions
Yes. If $K \subset \Omega$ is compact, let $\text{dist}(K, \Omega^c) > r > 0$. Then for any $p \in K$ and convex function $f$, $f(p)$ is bounded above by the average of $f$ over the ball of radius $ …
2
votes
Accepted
Determining the sign of each element of the optimal of a strict convex function
Try $d = 2$, $f(x_1,x_2) = (x_1+1)^2 + (x_1+1)(x_2 + t) + (x_2 + t)^2$ where $t$ is a parameter. The minimum is at $(-1,-t)$. The sign of the second component of the gradient changes at $t = -1/2$, …
4
votes
How to examine the convexity of a complex function numerically?
If your function was strictly convex and $C^3$ (let's say on a compact interval $I$) and you had bounds on the third derivative there, it might be possible
to prove its convexity on $I$ by evaluating …
5
votes
Possible to find a set of log-concave functions with log-concave sums?
In fact $\ln(x^2 + x^\beta)$ is concave for $x > 0$ iff $3-2\sqrt{2} \le \beta \le 3 + 2 \sqrt{2}$.
This comes from writing
$$ \dfrac{d^2}{dx^2} \ln(x^2 + x^\beta) = \dfrac{x^2}{(x^2 + x^\beta)^2} \l …
2
votes
Accepted
Convex functions: bounding the difference
$$f(x) - f(x') \le \sum_i \left(f(x_i) - f(x')\right)$$
iff the function $g(x) = f(x + x') - f(x')$ is subadditive.
1
vote
Accepted
Proving that a specific function is quasiconvex
It's not true.
Consider the $2 \times 2$ matrix $$F(X) = \pmatrix{f(X) & 0\cr 0 & f(X-2)\cr}$$
where $f$ is an even function, everywhere $> 0$, and decreasing on $[0,\infty)$. Take $a = (1,1)^T$.
T …