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### subdifferential of $\max_{i=1,\cdots,k} x_i+\frac{1}{2}\|x\|_2^2,\ \ \ x\in \mathbb{R}^n$

How to find the subdifferential of $$f(x) = \max_{i=1,\cdots,k} x_i+\frac{1}{2}\|x\|_2^2,\ \ \ x\in \mathbb{R}^n$$ My derivation is: $\nabla \frac{1}{2}\|x\|_2^2=\nabla \frac{1}{2}x^Tx=x$ ...
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### More understanding about $E_u[\partial_x h(x,u)]$, $u$ is a random variable

Consider the subdifferential "$\partial_x h(x,u)$", $u$ is a random variable. (Note: subdifferential is a set with the definition in subgradient method.) How to understand $$E_u[\partial_x h(x,u)]$$ ...
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In these notes (section 2.3), it is stated that: A point $x^*$ is a minimizer of a function $f$ (not necessarily convex) if and only if $f$ is subdifferentiable at $x^*$ and $0 \in\partial f(x^*).$...
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### Lower bound of averaging gradient method (Prof. Yurii Nesterov's paper)

I am reading the paper of Prof. Yurii Nesterov: Primal-dual subgradient methods for convex problems The last inequality confuses me: (p.231) Note: 1. The ...
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### Subdifferential optimality conditions

I need help with subdifferential optimality. Let $f(x_1, x_2)=x_1^2 + x_2^2 + |x_1 -x_2 - y|$. Find: \begin{align} \min_{x_1, x_2} f(x_1, x_2) \end{align} This is convex, so must have unique ...