I originally bought this book as a source of remedial study following the end of my US state school CS undergrad experience (I completed a BS Computer Science in 2013, &gt; 3.5 GPA), and as preparation for V1 &amp; V4 of TAOCP. I use the word remedial here in the sense that I felt that I was missing a critical foundation in the mathematical analysis and derivation of algorithms, even after the course-work of that degree. I've done 3 chapters of it in full, and I will mention a number of things that other reviews haven't talked about. Due to the horrendous time commitment of this book, I strongly suspect this is because those reviewers haven't actually worked through it, and have instead chosen to skim and allow Knuth fanboyism, along with the desire for mutual association, to cloud their opinions. I'm revoking a star simply to balance those reviews out, and so that graduates in similar situations have an actual informed critical opinion of this book to find among the reviews.<br><br>Take-Aways (As of Ch 3):<br><br>There are many aspects of summations, integer functions, and proofing that: I never saw covered in my CS degree, are unforgettable, and can be immediately applied to most algorithm research. Those alone make this book worth every penny. Further, the problems posed by this book are more than just repeated mechanics, as I have seen in books like those mentioned below. Each problem is carefully chosen, thorough, and exposes multiple aspects of each topic. They really do weed out many faults that I wasn't really exposed to- as a small example: the importance of ensuring validity of n-1 and n-2 hypothesis &amp; base cases during an induction proof.<br><br>The Bad:<br><br>Students educated through a contemporary CS track at most American uni's, I believe, (e.g. Rosen Discrete Math, Cormen Algorithms) will find this book both terrifyingly terse and frustratingly paced. In many cases, examples are given without derivation. In many cases, important points are made without obvious connection to previous topics. This is not without a solution however, and getting through this book is often an acquired technique of paper noting things as-you-go, as well as a learned hyper-literacy. The terseness is also a double-edged sword, as sometimes I found it useful as an extra opportunity to practice the taught methods to see if I could come to the same result. Further, the reader should be prepared to go back and review propositional logic &amp; university calculus theorems (atleast FTC, definite vs indefinite integrals). For example, the description of sum by parts in the section on finite calculus  assumes _much_ from the reader, and being able to use university calc. as a point of reference to get through that is helpful.<br><br>A lot of exercises are tersely explained in both problem and solution. Further, many solutions are totally left-field (having little to do with material in the book). This isn't necessarily bad, as even taking the wrong path to a solution is very educational. However, at some point the reader has to make a judgment as to how long to commit to a certain problem. Many terse problems &amp; left-field solutions instill the wrong judgment: quitting too early.<br><br>Conclusion:<br><br>Attention to detail &amp; extra work is necessary to overcome the terseness of this particular beast, but it's worth it. I recommend this book for developers confronted with algorithm optimization problems, as a well as for a different take on parts of discrete math, and definitely for students coming out of a US state school CS program, the last which this book complements very well. Having worked through some of V1 TAOCP, I would also say that the book is effective in expanding upon its math underpinnings (V1 at-least), and incidentally, does give one confidence to tackle Knuth's other works.