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The aim of this book is to teach mathematics students how to program using their Three methods for constructing an algorithm or a program are. Universitext.
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We also discuss matchings, covering, bipartite graphs. Read more Read less. Prime Book Box for Kids. Customers who viewed this item also viewed. Page 1 of 1 Start over Page 1 of 1. Logic for Computer Science: Geometric Methods and Applications: Sponsored products related to this item What's this? Deep Learning for Computer Vision: Expert techniques to train advanced neural netwo Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks. Your one stop solution to using Python for network aut Machine Learning and Deep Learning with Python, scikit-lea Unlock modern machine learning and deep learning techniques with Python to empower your business with cutting-edge skills.

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Write a customer review. There was a problem filtering reviews right now. Please try again later. Discrete mathematics so-called because it handles a bunch of seemingly "random" topics is the red-headed stepchild of math; nearly every computer science student is required to take a course in it, but there is little glamor to it and so the textbooks about it are usually very wooden.

Jean Gallier, who teaches at Penn, has tried to duplicate the flavor of an introductory yet high-powered course in this Springer Universitext; however, those of you who aren't "Ivy Leaguers" will not be particularly well-served by his presentation. Gallier writes in an "engaging" style but four hundred and fifty pages later the reader is not yet particularly engaged; the topics covered in this way come across as a weird mix of very basic principles of modern math and highly abstract topics which are mostly gestured at.

The book starts with a chapter on logic that is far too advanced; I cannot imagine that even an average computer science PhD will need to know Kripke's semantics for intuitionistic logic.

Discrete Mathematics (Universitext): Jean Gallier: Books

It is then followed by sections on set theory which are too primitive; it seems likely that even a four-year CS student will need to know several versions of the axiom of choice. The two chapters on graph theory are adequate, although too little care is taken in presenting the graph-theoretic algorithms programmers may very well have a need to use in practice; another chapter goes on about the RSA public-key cryptography system at length, then directs the reader to "more complete" discussions of the system in other books.

The section on lattices, a topic where a great deal of "pure" logic is naturally and neatly encapsulated in a properly "mathematical" structure, is the best of the bunch. For a person like me with limited knowledge of the "purer" mathematical topics addressed there is some value to this as a "starter", but another book will be required to achieve an adequate mastery of the field.

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See and discover other items: Up until the fall of my junior year, I mostly stuck to this plan apart from some silly distributional requirements. But then I got burned out. Did a little too much math in the fall of my junior year, and decided it really wasn't for me. So I took a chance and took some computer science courses. I can't speak to whether or not it will be useful for math research, although my intuition tells me that it most likely wouldn't hurt.

I found the theoretical computer science courses graphs and networks, design and analysis of algorithms to be very interesting and fun, and it wasn't until after I took some CS that I really understood how poorly I understood computers. I can't say that taking computer science has deepened my ability to do mathematics, but it's certainly exposed me to a new branch of math theoretical CS , given me many useful tools for doing math especially stat , and even if you don't think it will be the most useful professional skill which, for most jobs, it should be , it's still a great life skill to have.

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I won't guarantee that it will be useful for your math research, but I will say that I can't imagine how the knowledge won't benefit you in a substantial way in your future. Thank you for your interest in this question. Because it has attracted low-quality or spam answers that had to be removed, posting an answer now requires 10 reputation on this site the association bonus does not count.

Would you like to answer one of these unanswered questions instead? Home Questions Tags Users Unanswered. Some mathematicians use programming every day, others never use it in their research. Mathematicians employed outside academia almost always use it. If you are a freshman, you don't yet know what you will be doing later.

So take a programming course! You may want to browse some of my answers, especially ones with the combinatorics tag; I often use short programs to find initial terms of sequences, explore hypotheses, search for counterexamples, generate tables and graphs, I think I could probably copy here my answer to the converse question on Programming, but I'm lazy so I'll just link to it: For what it's worth, I happen to disagree with points from all 8 at the moment answers written below. But I agree completely with GEdgar's comment above. The answer is definitely yes, and there are many reasons. The three most important are: The bigger your "toolset" is, the more you can do.

You do not know what you will be doing in the future. To be more specific, I will just name a few concrete cases: It is much easier to verify multiple cases using computer, e. Computer can solve symbolically many tedious things fast, things that would take you weeks or even months to calculate by hand, e. Every mathematical software Maple, Matlab, Mathematica, but also Sage, Octave, and so on are based on a programming language that you use to tell the program what you want to do.

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Many mathematical problems are too hard to solve symbolically, but often you can find numerical solutions with arbitrary precision. A number of math-related topics or other domains that extensively use math nowadays, like computational biology, meteorology, financial analysis, quantum physics, Using computer you can visualize your results to gain intuition, or to present it to a wider audience, etc. Trust me, it really does help, knowledge of a programming language will help you here a lot, e.

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This is what computers are really good at, so if you need to preform some well defined tasks on large sets of data, just make computer do your work. However, usually in a research there are no tools that would do exactly what you want, just some building blocks of some sort, so you need to know how to use them and build even more awesome things. Also, you have no idea where life will throw you, it is good to know that skilled programmers and big part of this skill is keen mind and approach to problem solving earn a lot of money ;- Programming can be rewarding on its own, especially if you use nice tools.

For a mathematician, I would recommend you a functional programming language, e. As the field is very large, people tend to differentiate, but there are still areas where there is no boundary between. I would not say that solving an ODE ought to require programming skills - typing it into Wolfram Alpha will work just as well. LaTeX is a mark-up language, not a programming language. The syntax to format a page nicely in LaTeX is something that most researchers will need to learn, but this not at all related to a programming course. One can't always trust what the computer spits out, though.

Programming for Mathematicians (Universitext)

If you can't program, how do you know if the mathematics itself or how you presented it to the computer is what's giving you problems? Ronald It is a markup language, but it is also a programming language. And actually it is not just conceptual, you sometime have to write complexe pieces of code in order to properly display, auto-generated data. Programming helps you do experimental math.

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To prove something you first need to find something to prove. You need to experiment, build some intuition, Sure, occasionally it is possible to compute enough many example cases by hand, but there are limits to that. Scott k 39 Dirk 8, 23