bv_cvxbook_extra_exercises

Heres the catch some of the measurements have been

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Unformatted text preview: ), where W ≻ 0 is the covariance matrix. The matrix A ∈ Rn×n is called the dynamics matrix or the state transition matrix, and the matrix B ∈ Rn×m is called the input matrix. You are given accurate measurements of the state and input signal, i.e., x(1), . . . , x(T ), u(1), . . . , u(T − ˆ ˆ 1), and W is known. Your job is to find a state transition matrix A and input matrix B from these data, that are plausible, and in addition are sparse, i.e., have many zero entries. (The sparser the better.) By doing this, you are effectively estimating the structure of the dynamical system, i.e., you are determining which components of x(t) and u(t) affect which components of x(t + 1). In some applications, this structure might be more interesting than the actual values of the (nonzero) ˆ ˆ coefficients in A and B . 46 By plausible, we mean that T −1 t=1 ˆ ˆ W −1/2 x(t + 1) − Ax(t) − Bu(t) 2 2 ≤ n(T − 1) + 2 2n(T − 1). (You can just take this as our definition of plausible. But to explain this choice, we note that when ˆ ˆ A =...
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