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We know that all the gradient of convex functions $f: X \to \mathbb{R}, X$ convex, are monotone maps, that is,

$f$ convex, continuously differentiable $\implies (\nabla f(x) - \nabla f(x^\prime)^T(x-x^\prime) \geq 0$

[Boyd Page 115 https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf]

We can make this statement stronger,

$f$ strictly convex, cont. diff. $\implies (\nabla f(x) - \nabla f(x^\prime)^T(x-x^\prime) > 0, \forall x$ such that $x \neq x^\prime$

$f$ strongly convex, cont. diff.$\implies (\nabla f(x) - \nabla f(x^\prime)^T(x-x^\prime) \geq c\| x-x^\prime\|, c>0, \forall x, x^\prime$

What about?

$f$ ??? continuously diff.$\implies (\nabla f(x) - \nabla f(x^\prime)^T(x-x^\prime) \geq -c\| x-x^\prime\|, c>0, \forall x, x^\prime$

What condition must $f$ satisfy? Is this the case when $f$ is strongly concave?

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  • $\begingroup$ You are missing squares on the norms. $\endgroup$ – gerw May 10 at 11:28
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The statement on the right-hand side in the last box (with $||x-x'||^2$) is equivalent to $f + \frac c2 \,\|\cdot\|^2$ being convex.

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  • $\begingroup$ What could be an example of such function? $\endgroup$ – Roy Ayers May 10 at 20:30
  • $\begingroup$ $f \equiv 0$ or $f = c \, \|\cdot\|^2$ or (more generally) any smooth function whose Hessian is bounded from below (i.e., the smallest eigenvalue is bounded from below). $\endgroup$ – gerw May 11 at 11:04

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