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December 16th, 2007, 06:03 AM  #1 
Newbie Joined: Dec 2007 Posts: 3 Thanks: 0  Optimization of a Matrix with one constraint.
Hello! I have a minor problem, that wont get solved... Its been posted in an old competition at our university, but I didnt manage to solve it in time. Maybe someone in here could help me? Where D is a symmetric n x n matrix, and x is an unknown n x 1 vector. I can only figure out how to test for a maximum later on, but is there something im missing with the charataristic roots? Regards 
December 16th, 2007, 04:56 PM  #2 
Global Moderator Joined: May 2007 Posts: 6,703 Thanks: 669 
I must confess I'm a little rusty here. However by taking first derivatives with respect to each of the xi, you will end with a matrix equation with (x1,....,xn) any eigenvector, as a solution. Normalize the eigenvectors to unit length and see which one gives you the maximum.

December 16th, 2007, 10:37 PM  #3 
Newbie Joined: Dec 2007 Posts: 3 Thanks: 0  
December 17th, 2007, 01:11 PM  #4 
Global Moderator Joined: May 2007 Posts: 6,703 Thanks: 669 
If you look at the line which says "D is symmetric" and you make the equations into one big vector, you will have Dx=Lx (L=lambda), which means the solution is an eigenvector and L is an eigenvalue. In general there will be n solutions.


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