bv_cvxbook_extra_exercises

# But this is not practical unless m and k are both

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Unformatted text preview: A and B = B , the left-hand side is χ2 , with n(T − 1) degrees of freedom, and so has mean n(T − 1) and standard deviation 2n(T − 1). Thus, the constraint above states that the LHS does not exceed the mean by more than 2 standard deviations.) ˆ ˆ (a) Describe a method for ﬁnding A and B , based on convex optimization. We are looking for a very simple method, that involves solving one convex optimization problem. (There are many extensions of this basic method, that would improve the simple ˆ ˆ method, i.e., yield sparser A and B that are still plausible. We’re not asking you to describe or implement any of these.) ˆ (b) Carry out your method on the data found in sparse_lds_data.m. Give the values of A and ˆ B that you ﬁnd, and verify that they are plausible. In the data ﬁle, we give you the true values of A and B , so you can evaluate the performance ˆ of your method. (Needless to say, you are not allowed to use these values when forming A and ˆ .) Using these true values, give th...
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## This note was uploaded on 09/10/2013 for the course C 231 taught by Professor F.borrelli during the Fall '13 term at University of California, Berkeley.

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