There is. My recollection is that it's easiest to use the adjugate like they do on this question: https://math.stackexchange.com/questions/38701/how-to-calcul

There is. My recollection is that it's easiest to use the adjugate like they do on this question: https://math.stackexchange.com/questions/38701/how-to-calculate-the-gradient-of-log-det-matrix-inverse https://math.stackexchange.com/questions/38701/how-to-calcul... Though, there are other ways to derive it. My personal opinion is that the vector and matrix calculus derivations in the book are too verbose, but this style may be more comfortable for some readers. My personal opinion is that the semidefinite and cone optimization communities have more concise ways of deriving these kind of derivatives and relationships. For example, this can be seen in Boyd and Vandenberghe's Convex Optimization or Ben-Tal and Nemirovski's Lectures on Modern Convex Optimization.