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I was going through Stephen Boyd's lecture on convex optimization. However, I am a bit confused about a problem

Given Minimize $f(x) = x_1^2+x_2^2$

subject to $f_1(x) = \frac{x_1}{1+x_2^2} \leq 0$

How come $f_1(x)$ is not convex?

I was going through Stephen Boyd's book related to convex optimization http://www.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf

Here is the exact screenshot of the page

enter image description here

user34790
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2 Answers2

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Look at $f_1$ along any "vertical" line $x_1=c$ where $c\neq0$. For positive $c$, you get a failure of convexity near the $x_1$-axis, and for negative $c$ you get a failure of convexity far from the $x_1$-axis.

Andreas Blass
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A set is convex if contains every convex combination of points within the set. If you plot $f_1$, then you get the following image.

enter image description here

We can see from this image that the set is not convex. In particular, the line segment between a point on the top right of the set and a point on the bottom right of the set is not included in the set.

If you want, you can also use this image to choose points x and y as well as $\lambda \in [0, 1]$ such that $f(\lambda x + (1- \lambda)y) \not\leq \lambda f(x) + (1 - \lambda) f(y)$ -- this will prove that the function is not convex using the definition of a convex function.

Tori
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