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Optimization with convex constraints and convex objectives; notions related to convex optimization such as sub-gradients, normal cones, separating hyperplanes

2 votes
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159 views

Lagrangian multipliers and a variant of Newton's method

A variant of Newton's method for solving the equality constrained problem \begin{equation} \begin{array}{ll} \min &f(x) \\ \text{s.t.} & h(x) = 0 \end{array} \end{equation} is as follows: \begin{equat …
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  • 97
1 vote
0 answers
34 views

Are such assumptions of functions similar to strong convexity reasonable in convex optimizat...

For $\mu$-strongly convex function $f:\mathbb{R}^d\to\mathbb{R}$, the following property holds: for any given $x,y\in\mathbb{R}^d$, we have $$ (\nabla f(x) - \nabla f(y))^\top(x-y) \ge \mu \|x-y\|^2.$ …
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  • 97