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Your problem with the book seems to be that it is general, rather than applicable to your particular field. Why on earth would expect that it would be? I took

by starwed 15y ago

Your problem with the book seems to be that it is general, rather than applicable to your particular field. Why on earth would expect that it would be? I took and enjoyed linear algebra using that text-- it didn't specifically address the QM I deal with as a physicist, but since the author can't predict each student's future, I'd hardly characterise that as a problem with the text! If it was used as a course on portfolios, ok, bad choice -- but directed against a general linear algebra text, your criticism doesn't seem very valid.

dxbydt 15y ago
>> rather than applicable to your particular field. No, I'm saying Hefferon is not applicable to any field. He eschews application in favor of formal theorem proving in vector spaces, in isolation of any real-life examples. That is great, but that is not the typical use case of anybody out there who actually uses linear algebra in industry, whether in computer graphics or portfolio theory or multivariate statistics or what have you.
- lotharbot 15y ago
  Being "not the typical use case... in industry" doesn't make it bad. It just makes it a pure mathematics book. Formal theorem proving in vector spaces is the typical use case of a pure mathematician; this book is for them. If you're looking to do application, use an applied book. Gilbert Strang's "Linear Algebra and its Applications" is quite good.
  - patrickc 15y ago
    I'll second that. Strang's book is by far my favorite introductory math book. He addresses fundamentals quite well, and gives good treatment to applications such as linear programming and game theory.
- T_S_ 15y ago
  Just to clarify for the crowd, portfolio theory, at least as far as its connection to linear algebra, is the mathematics of a covariance matrix. You are talking about different ways to decompose such a matrix. There are many ways and the justification for any of these methods goes beyond linear algebra and is really a special area of finance theory more than linear algebra. Long before reading the book, I spent a lot of time with financial modeling of all kinds, but your complaint doesn't resonate with me at all. I went on to study machine learning and found the ideas in Hefferon laid a good foundation. It might be fair to point out that is not a cookbook and you don't use it to learn how to wrangle LAPACK, but I think it's over the top to say what you just did.

by starwed 15y ago

dxbydt 15y ago

>> rather than applicable to your particular field. No, I'm saying Hefferon is not applicable to any field. He eschews application in favor of formal theorem proving in vector spaces, in isolation of any real-life examples. That is great, but that is not the typical use case of anybody out there who actually uses linear algebra in industry, whether in computer graphics or portfolio theory or multivariate statistics or what have you.

lotharbot 15y ago
Being "not the typical use case... in industry" doesn't make it bad. It just makes it a pure mathematics book. Formal theorem proving in vector spaces is the typical use case of a pure mathematician; this book is for them. If you're looking to do application, use an applied book. Gilbert Strang's "Linear Algebra and its Applications" is quite good.
- patrickc 15y ago
  I'll second that. Strang's book is by far my favorite introductory math book. He addresses fundamentals quite well, and gives good treatment to applications such as linear programming and game theory.
T_S_ 15y ago
Just to clarify for the crowd, portfolio theory, at least as far as its connection to linear algebra, is the mathematics of a covariance matrix. You are talking about different ways to decompose such a matrix. There are many ways and the justification for any of these methods goes beyond linear algebra and is really a special area of finance theory more than linear algebra. Long before reading the book, I spent a lot of time with financial modeling of all kinds, but your complaint doesn't resonate with me at all. I went on to study machine learning and found the ideas in Hefferon laid a good foundation. It might be fair to point out that is not a cookbook and you don't use it to learn how to wrangle LAPACK, but I think it's over the top to say what you just did.