A First Course in Linear Optimization (2013)
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A First Course in Linear Optimization (2013)
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Re: A First Course in Linear Optimization (2013)
#2From the dedication:
For students (even Ohio students). Not for publishers — not this time. Maybe next time.
Re: A First Course in Linear Optimization (2013)
#3If 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.
Re: A First Course in Linear Optimization (2013)
#4I 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.
Re: A First Course in Linear Optimization (2013)
#5Call 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.
Re: A First Course in Linear Optimization (2013)
#6Call 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.
It is written in LaTeX. It even says so in the first few pages.
Re: A First Course in Linear Optimization (2013)
#7Linear 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.
Re: A First Course in Linear Optimization (2013)
#8Linear 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.…
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.
Re: A First Course in Linear Optimization (2013)
#9Linear 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.…
We in cs theory are constantly emphasizing the universal nature of LPs and their generalizations :)
Re: A First Course in Linear Optimization (2013)
#10[deleted]