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- Read the book, it is very well written: Convex Optimization – Boyd and Vandenberghe
- http://videolectures.net/mlss07_vandenberghe_copt/ - this is a nice video of one of the authors of the book we are using
- Material of the first introductory tutorial: Optimization: Rayleigh-Ritz and Linear Algebra
- Here is my alternative proof for Ex. 3.57 in Boyd, i.e. a proof that is matrix convex on .
- Proof related to Boyd, Exercise 3.26 (a): where and This shows that the given supremum is really equal to the sum of the k largest eigenvalues.
- Additional proof for composition with affine mapping: is convex/concave, if is convex/concave:

- Try to use Matlab for your plots (such as the one in Ex. 5.26 in Boyd). For this exercise, the code could be:Then, rotate the plot manually.
[X,Y] = meshgrid(-2:.1:2,-2:.1:2); Z = X.^2+Y.^2; C1=(X-1).^2+(Y-1).^2-1; C2=(X-1).^2 +(Y+1).^2-1; p=0.*X; hold on; contourf(X,Y,Z); mesh(X,Y,C1); mesh(X,Y,C2); meshc(X,Y,p); axis([-2 2 -2 2 -2 2]); hold off;

- Matlab Code For Smoothness Regularization - as discussed in Ex08