- MIP Basics by Gurobi
- Convex Optimization Course by R. Tibshirani
- Non-convex Optimization by P. Jain and P. Kar
In trying to use planning for decision support systems, there have been an array of automated planning techniques and softwares that we have tried to use. Here is a list of things we believe others might find useful:
- Playground for classical planning -- http://planning.domains/ (try out the editor --> import)
- Planning resources -- https://planning.wiki/
- Check out the section on additional resources.
- Responsive Slack channel to ask for help.
- Efficient planners:
- Fast Forward (FF): https://fai.cs.uni-saarland.de/hoffmann/ff.html
- Fast Downward (FD): http://www.fast-downward.org/
- Easy to modify and understand
A quick guide to setup pytest for your python code. Convert your code into a package by using the following directory structure.
setup.py
src
-- /a/1.py
tests
-- test_a_1.py
The setup.py
file is configured as follows:
Generates a bipartite graph with vertex sets V1 an V2 and edge list E
\begin{figure}[t]
\centering
\begin{tikzpicture}
% V1
\foreach \i in {-2, -1, ..., 2} {
\node at (\i, 0) (v\i) {$v_{\i}$};
\fill (\i, -0.25) circle (1.5pt);
}
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