
A First Course in Linear Optimization (2013) - kneth
https://umich.app.box.com/s/aov81sye5qxlx0yyonhy23itstsjp4t4
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dtnewman
Linear optimization is a subject that is often neglected in computer science
curriculums, even at the graduate level. It is common for computer science
students to learn algorithms such as hill climbing or even stochastic local
search algorithms such as simulated annealing, but in fact there are many
applications for which linear optimization methods can solve the same problems
better in a small fraction of the time. If you are a computer scientist or a
software engineer with an interest in mathematical optimization, linear and
integer programming are must-have tools to round out your knowledge base.

A great way to get started is to play around with the solver feature in Excel.
Many software engineers may be loath to use this tool, but Excel actually
provides a great GUI with which to do simple linear and integer programming
problems.

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fizixer
I would make a stronger statement: numerical algorithms (computational
continuous mathematics) are seriously neglected in favor of combinatorial
algorithms (computational discrete mathematics).

Take any undergrad algorithms class: 99% of it is combinatorial. There's an
equal (probably larger) parallel universe of numerical thinking and numerical
algorithms which is either not taught, or taught as a secondary class under
the names of 'numerical analysis', 'scientific computing', and so on. None of
stacks, queues, trees, and graphs are going to help you out when you have to
discretize a differential equation or solve the resulting linear system with
correctly enforcing the boundary conditions. You could ace an algorithms class
and not have a clue how to begin tackling those kinds of problems.

This is especially relevant today when machine learning is heavily dependent
on computational linear algebra and other numerics, and a typical CS student
is not trained for that.

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graycat
> a typical CS student is not trained for that.

And what about a typical CS prof? No, I won't name any names, not today.

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rmxt
If you are interested in the above text, this textbook might also be of
interest: Applied Mathematical Programming by Bradley, Hax, and Magnanti for
MIT's Introduction to Optimization. (15.053). Not as code heavy, but still
enlightening.

[http://web.mit.edu/15.053/www/](http://web.mit.edu/15.053/www/)

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kaa2102
I studied Industrial Engineering & Operations Research in undergrad and grad
school. Optimization and mathematical modeling has been helpful in consulting,
problem-solving, business and programming. I highly recommend taking a deep
dive into this subject.

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elliott34
Call me a snob, but after reading too many beautifully typeset LaTeX
books/papers, trying to reading math in fonts like this is very off putting.

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sjtrny
It is written in LaTeX. It even says so in the first few pages.

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merraksh
From the dedication: _For students (even Ohio students). Not for publishers —
not this time. Maybe next time._

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graycat
He has one of the best backgrounds -- Cornell, Nemhauser, Bland, etc.

