Name: Nonlinear Programming
ISBN: 9781886529144

wcsunJun 2011

First, finish lectures by Professor Gilbert Strang. http://web.mit.edu/18.06/www/

To my memory, session notes of CS229 is good enough for understanding SVM and gaussian distributions. Also watch youtube videos. http://www.stanford.edu/class/cs229/materials.html http://www.youtube.com/watch?v=UzxYlbK2c7E

If you just want to use the libraries, you can stop here.

If you want to know more, read chapters 1-3 of nonlinear programming by Professor Dimitri Bertsekas before convex optimization. http://www.athenasc.com/nonlinbook.html

Then, you can try to finish EE364 and watch the videos. http://www.stanford.edu/class/ee364a/ http://www.youtube.com/watch?v=McLq1hEq3UY

If you want to roll your own algorithms, you have to know some optimization tools. http://cvxr.com/cvx/

And there is some statistics knowledge you have to fill in. I used these: http://www.stat.umn.edu/geyer/5101/ http://www.stat.umn.edu/geyer/5102/ R is used in the courses.

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sirwhinesalotSep 2024

Not the same person but maybe "Mixed-Integer Nonlinear Programming: A Survey of Algorithms and Applications" could be a good start? I haven't read it yet.

HiGHS is the best open source MILP solver at the moment, and MATLAB of all things has a pretty good explanation of how it works internally: https://nl.mathworks.com/help/optim/ug/mixed-integer-linear-...

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cashsterlingApr 2023

I'd say RL is an advanced robotics topic. There are some books out there that cover introductory or general RL aspects... after that you'll have to dive into papers and specific code bases.

Might not be a bad idea to start with a Python-based, practical RL book and move from practical demonstrations towards theory.

Although not an RL book, I really like Data-Driven Science and Engineering by Brunton and Kutz:

- https://www.databookuw.com/

- https://www.amazon.com/Data-Driven-Science-Engineering-Learn...

Steve Brunton also has an awesome youtube channel on dynamic systems, control, and machine learning.

Robotic books with some RL concepts (don't have either of these tbh):

- https://introduction-to-autonomous-robots.github.io/

- https://mitpress.mit.edu/9780262046169/learning-for-adaptive...

General texts/resources on adaptive control, optimal control, and RL:

- https://arxiv.org/abs/1912.03513

- http://www.cs.cmu.edu/~cga/dynopt/

- http://www.mit.edu/~dimitrib/RLbook.html

Practical Methods for Optimal Control Using Nonlinear Programming, Third Edition by John Betts:

- https://my.siam.org/Store/Product/viewproduct/?ProductId=316...

Adaptive Control Tutorial by Ioannou and Fidan

- https://mitpress.mit.edu/9780262039246/reinforcement-learnin...

Control Systems and Reinforcement Learning by Sean Meyn looks to be on topic but I haven't read through it.

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bkcooperJul 2021

Nocedal and Wright is good. +1 also to the suggestions for Boyd and Vandenberghe. I really like Boyd's writing in general; he has coauthored some good review articles on proximal algorithms and ADMM.

A couple of other suggestions:

Nesterov's Introductory Lectures on Convex Optimization. This one is pretty tough sledding, but I found the perspectives in the first chapter particularly to be enlightening. It seems like there's a newer Springer book which is probably an expansion on this.

Bertsekas's Nonlinear Programming. Bertsekas has written a lot of books, and there's a fair amount of overlapping going on. This one seemed to be the one that has the most nuts and bolts about the basics of optimization.

EDIT: If you want more understanding of convexity beyond what's presented in these books, Rockafellar's Convex Analysis is helpful.

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ablealApr 2010

Side-issue: why isn't Golden section better known ?

Luenberger's book [1] convinced me that a line search should be done with golden section, when I was hacking non-linear optimization. Yet I rarely see it mentioned - seems always to come down to binary search ...

[1] http://www.amazon.com/Linear-Nonlinear-Programming-David-Lue...

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