A cursory look on the book: Computer science theory for information age, by Ravi Kannan and Hopkroft

[Dilawar Singh]

During an online course on Functional Programming, the instructor made
an interesting comment that "Computer science is a funny name for this
descipline for it is neither about computers, nor it is a science."
Computer Science may not be about computers, but it is definately
about data and algorithms. The authors of this book have computer and
its uses on their mind (Both spends quite a time solving industrial
problems). They write that, "although programming languages and
compilers are still important and highly skilled individuals are
needed in these area, the vast majority of researchers will be
involved with applications and what computers can be used for, not how
to make computers useful."

The usual course of "introduction to algorithm" is concerned about how
to make computer useful for computation. For this purpose one
concentrates on algorithms and pass comments, whenever appropriate, on
data-structures. This has been changing for performance of many
algorithms depends heavily on how one arrange large data (both in
theory and in computer memory). This book is data-centric. This is not
to say that algorithms are lightly treated.

This book contains enough material to serve as a second book on
algorithms but it does not look like an replacement of classical book.
It can surely be read after "introduction to algorithm" by Cormen et.
al or some other good book. I am aware of only this and TAOCP by
Knuth.

The purpose of this book is explore how to make computer more useful.
And the authors further comments on it, "we have written this book to
cover the theory likely to be useful in the next 40 years just as
automata theory and related topics gave students an advantage in
the last 40 years. One of the major changes is the switch from
discrete mathematics to
more of an emphasis on probability and statistics. Some topics that
have become important are high dimensional data, large random graphs,
singular value decomposition along
with other topics covered in this book."

This free book [1] talked about here looks at algorithm from this
point of view. I read only two chapters so far and they are pretty
interesting. It is also quite readable. Major part of this book is an
outcome of their work related to searching large data; and some
examples are given in the introduction of this book. Even if one is
not interested in theory and gory details, one can greatly benefit
from reading just the introduction. Each chapters have problems to
solve. The appendix can serve as good notes taken by a really serious
student on first course on algorithm.

I have only worked out section on clustering and have read the
section on graphs which some concentration; both chapters are lucidly
written. One is confident that other chapters should be equally
readable, at least for those who are inititated into those areas.

[1] http://www.cs.cmu.edu/~venkatg/teaching/CStheory-infoage/hopcroft-kannan-feb2012.pdf

Meanwhile a blogger cracked the following joke.

A linguistics professor was lecturing to his class one day. In
English, he said, a double negative forms a positive. In some
languages though, such as Russian, a double negative is still a
negative.
However, he pointed out, there is no language wherein a double
positive can form a negative.
A voice from the back of the room piped up, “Yeah, right.”

--
Dilawar
NCBS