The subgradient tag has no wiki summary.
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Calculation of the sub gradient of the first norm of a matrix
Lets say I have a matrix X and its first norm $||X||_1$. How do I calculate the subgradient of this norm with respect to matrix X itself.
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Nonlinear optimization of constraint parameter - subdifferential?
Disclaimer: I discovered that the FAQ suggests to post research-level to mathoverflow instead of math.stackexchange. I "moved" the question accordingly, cp. post at mathoverflow. Sorry for the ...
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Subgradient of convex minimization duality
$$\min(f_0(x))$$
$$\text{s.t. }f_i(x) \le y_i \forall i, i = 1 ,\ldots, m$$
$$f_i : \text{convex};\quad x : \text{variable}$$
It is also considered that $g(y)$ is the optimal value of the problem ...
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Gradient Descent for Primal Kernel SVM with Soft-Margin(Hinge) Loss
Given the primal objective
$$F({\bf a})=L\sum_{i,j}a_{i}a_{j}k(x_i,x_j) + \sum_{i}max(0, 1-y_i \sum_{j}a_jk(x_i,x_j)$$
for the soft margin SVM, where ${\bf a}=(a_1,...,a_N)$, N being the number of ...
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Why is the set of subgradients a convex set?
I'm struggling to understand an example we were given. The problem description is:
Let $f$ be a convex function in $E^n$. Prove that the set of subgradients of $f$ in a given point form a ... convex ...
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Mathematical applications of the subgradient
Do you know mathematical results which can be nicely proved using subgradient?
For example, Jensen's inequilaty can be proved like that: Let $\varphi : \mathbb{R}^n \to \mathbb{R}$ be a convex ...
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Explain $x^*$ of subgradient in KKT -conditions: primal optima or dual optima?
I asked this question here but I noticed that this notation $x^*$ may actually mean two things: primal optimality and dual optimality. Please, explain this notation particularly here: I understand ...
