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 ,
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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- Fall '13
- The Aeneid