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Algorithms: A Functional Programming Approach
The design of algorithms for problem solving lies at the heart of computer science. Concise, yet authoritative, this book teaches the skills needed to master this essential subject. The authors challenge more traditional methods of teaching algorithms by using a functional programming context, with Haskell as the implementation language. This leads to smaller, clearer and more elegant programs which enable the programmer to understand the algorithm itself more quickly and use that understanding to explore alternative solutions. Placing the emphasis on program development rather than the mathematical properties of algorithms, the book uses a succession of practical programming examples to develop in the reader problem-solving skills which can be easily transferred to other language paradigms.
256 pages, Paperback
First published July 21, 1999
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February 9, 2010This was quite useful for me as a Haskeller seeking a refresher in some of undergraduate computer science stuff. It has the virtue of being very concise, yet at the same time broad and very concrete.
Everything is explained in code. I think this is partly made possible by the authors' choice of a functional programming language. For example, one trick they use in this book is to take an algorithm design paradigm like "divide and conquer" or "dynamic programming" and implement the paradigm itself as a higher order function. Then implementing dynamic programming algorithms is just a matter of passing in little domain-specific functions as arguments.
The book also helped me to think a little bit more clearly about trickier things like strictness and laziness. It also quite breezily used techniques like "tying the knot" without making its seem particularly difficult.
I'm not sure how well this would suit somebody with a different background, but this pretty much the kind of thing I was looking for :-)
Everything is explained in code. I think this is partly made possible by the authors' choice of a functional programming language. For example, one trick they use in this book is to take an algorithm design paradigm like "divide and conquer" or "dynamic programming" and implement the paradigm itself as a higher order function. Then implementing dynamic programming algorithms is just a matter of passing in little domain-specific functions as arguments.
The book also helped me to think a little bit more clearly about trickier things like strictness and laziness. It also quite breezily used techniques like "tying the knot" without making its seem particularly difficult.
I'm not sure how well this would suit somebody with a different background, but this pretty much the kind of thing I was looking for :-)
January 1, 2013
I started reading this book and I really enjoyed it. I will definitely check this book out in the near future.
I need all the help I can get when it comes to algorithms.
I need all the help I can get when it comes to algorithms.
November 10, 2015
An excellent read on algorithms in Haskell, which is wonderful since Standard ML makes me break out into hives.
While written in 1999, Algorithms: A Functional Programming Approach still manages to be current, except (forgivably) on the subject of parallelism and concurrency, which it views as newfangled and prohibitively expensive. This can be forgiven since I think I got my first dual-core processor in 2002...
I found this to be a much more approachable text than the classic Introduction to Algorithms, which tends to get lost in the iterative weeds much of the time and (probably as a consequence) had to spend much more time on their lemmas and theorems to prove correctness.
While written in 1999, Algorithms: A Functional Programming Approach still manages to be current, except (forgivably) on the subject of parallelism and concurrency, which it views as newfangled and prohibitively expensive. This can be forgiven since I think I got my first dual-core processor in 2002...
I found this to be a much more approachable text than the classic Introduction to Algorithms, which tends to get lost in the iterative weeds much of the time and (probably as a consequence) had to spend much more time on their lemmas and theorems to prove correctness.
Displaying 1 - 3 of 3 reviews