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## Main Question or Discussion Point

Hey!

Happy New Year 2013 to all of you!

I am in good mood for learning something new so I need advice.

I'm currently watching the videos for Machine learning from Stanford University, but I'm stuck at Lagrangian multipliers and duality.

I got solid background in Calculus I and II (I read and learned from the book by Irl Bivens and I really like it) but I've never learned about Lagrange multipliers nor Matrix Calculus, and now it's time to step my Calculus up

Also I got solid basic background in Linear Algebra but never learned about Eigenvalues and Eigenvectors or Semideﬁnite Matrices.

So I need some book for self-studying:

Here are the topics that are new to me:

Matrix Calculus 20

4.1 The Gradient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.2 The Hessian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.3 Gradients and Hessians of Quadratic and Linear Functions . . . . . . . . . . 23

4.4 Least Squares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.5 Gradients of the Determinant . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.6 Eigenvalues as Optimization

3.10 The Determinant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.11 Quadratic Forms and Positive Semideﬁnite Matrices . . . . . . . . . . . . . . 17

3.12 Eigenvalues and Eigenvectors . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.13 Eigenvalues and Eigenvectors of Symmetric Matrices . . . . . . . . . . . . . 19

Convex Optimization

Convex Optimization Part 2

Here are the materials from the course (there are documents in Section Notes).

Thanks a lot.

Regards.

Happy New Year 2013 to all of you!

I am in good mood for learning something new so I need advice.

I'm currently watching the videos for Machine learning from Stanford University, but I'm stuck at Lagrangian multipliers and duality.

I got solid background in Calculus I and II (I read and learned from the book by Irl Bivens and I really like it) but I've never learned about Lagrange multipliers nor Matrix Calculus, and now it's time to step my Calculus up

Also I got solid basic background in Linear Algebra but never learned about Eigenvalues and Eigenvectors or Semideﬁnite Matrices.

So I need some book for self-studying:

Here are the topics that are new to me:

Matrix Calculus 20

4.1 The Gradient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.2 The Hessian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.3 Gradients and Hessians of Quadratic and Linear Functions . . . . . . . . . . 23

4.4 Least Squares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.5 Gradients of the Determinant . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.6 Eigenvalues as Optimization

3.10 The Determinant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.11 Quadratic Forms and Positive Semideﬁnite Matrices . . . . . . . . . . . . . . 17

3.12 Eigenvalues and Eigenvectors . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.13 Eigenvalues and Eigenvectors of Symmetric Matrices . . . . . . . . . . . . . 19

Convex Optimization

Convex Optimization Part 2

Here are the materials from the course (there are documents in Section Notes).

Thanks a lot.

Regards.