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The logistic loss function is: $$\mathcal{L}=\frac{1}{n}\sum_{i=1}^n\log(1+\exp(-y_ix_i^T\theta))$$ in which $y_i\in\{-1,+1\},x\in \mathbb{R}^d$. How to show that $\mathcal{L}$ is strongly convex.

My thinkings: Can we get the $\nabla^2 \mathcal{L}(\theta)$ and show $\nabla^2 \mathcal{L}(\theta)-mI$ is PSD for some $m$?

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  • $\begingroup$ Yes, you can compute the hessian wrt $\theta$ and it will be straightforward to see it is PSD. Computing it is a little bit of a hassle but does have a nice closed form expression $\endgroup$
    – Casey
    Commented Mar 15, 2019 at 12:33
  • $\begingroup$ The computation of the gradient and Hessian of logistic loss function is given here math.stackexchange.com/questions/3098910/… $\endgroup$
    – user550103
    Commented Mar 16, 2019 at 8:37
  • $\begingroup$ I just come across this problem again three years later. Is there any condition that we can use to make it strongly convex? $\endgroup$
    – maple
    Commented Aug 31, 2022 at 21:55

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It is not strongly convex. Take $n=d=1$. You are getting a function of the form $f(x)=\log(1+\exp( a x))$. Its second derivative is $$ f''(x) = \frac{a^2 \exp( a x) } { (1 + \exp(ax))^2} $$ Assuming $a > 0$, you have $\lim_{x \to -\infty} f''(x) = 0$. Thus, there is no positive constant which bounds $f''$ from below. A similar argument shows the same if $a < 0$.

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  • $\begingroup$ You're absolutely right. And yet: I think it may be possible to prove strong convexity in the multidimensional case with certain conditions on $(y_i,x_i)$. I don't know this for sure. But this is certainly the case that's most interesting in machine learning applications. $\endgroup$ Commented Mar 18, 2019 at 15:36
  • $\begingroup$ @MichaelGrant intuitively, i do not believe so. Since each term of the log-loss behaves, approximately, like $\max(0, -y_i x_i^T \theta)$, and therefore any $\alpha \|x\|^2$ eventually 'curves up' faster. $\endgroup$ Commented Mar 18, 2019 at 18:03

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