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In many books, Kullback-Leibler Divergence is equal to Relative Entropy. $$ D_{kl}(u,v) = \sum_{i=1}^n(u_ilog(u_i/v_i). $$ However, I find in the book, Convex Optimization (Stephen Boyd) page 90, the KL Divergence is defined as, $$ D_{kl}(u,v) = \sum_{i=1}^n(u_ilog(u_i/v_i)-u_i+v_i). $$ Why KL Divergence has these two different definition? Which one is correct?

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    $\begingroup$ The book (also written by Vandenberghe) clearly mentions "Note that the relative entropy and the Kullback-Leibler divergence are the same when u and v are probability vectors, i.e., satisfy 1Tu = 1Tv = 1.)" $\endgroup$ – LinAlg Dec 20 '18 at 2:45
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They're equivalent because $\sum_i u_i=\sum_i v_i=1$.

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  • $\begingroup$ thank you so much. $\endgroup$ – Alan Dec 19 '18 at 23:15
  • $\begingroup$ equivalent only for probability vectors, as the book mentions $\endgroup$ – LinAlg Dec 20 '18 at 2:45

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