bv_cvxbook_extra_exercises

k j li where e i li is the set of edge indices

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Unformatted text preview: er and demand forecasting, and so on.) Validation. After finding an optimal value of c, based on the set of samples, you should double check or validate your choice of c by evaluating the overall cost on another set of (validation) ˜ samples, (˜(j ) , d(j ) ), j = 1, . . . , N val , a C val 1 = b c + val N N val T j =1 ˜ p( d (j ) − c T a (j ) ) + . ˜ (These could be another set of historical data, held back for validation purposes.) If C sa ≈ C val , our confidence that each of them is approximately the optimal value of C is increased. Finally we get to the problem. Get the data in energy_portfolio_data.m, which includes the required problem data, and the samples, which are given as a 1 × N row vector d for the scalars d(j ) , and an n × N matrix A for a(j ) . A second set of samples is given for validation, with the names d_val and A_val. Carry out the optimization described above. Give the optimal cost obtained, C sa , and compare to the cost evaluated using the validation d...
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