Linear Programming in Python (2023)
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Linear Programming in Python (2023)
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Re: Linear Programming in Python (2023)
#2I have often started modeling problems with pulp. As the problem size gets bigger, pulp will fail to work —- that is, pulp handles writing out large problems to disk (nlogn complexity) worse than gurobi handles reading and solving them (expn complexity). Then I end up writing out the problems into text files on python „by hand“, which is very cumbersome.
I wish python and it’s libraries wasn’t so inefficient.
Re: Linear Programming in Python (2023)
#3I have often started modeling problems with pulp. As the problem size gets bigger, pulp will fail to work —- that is, pulp handles writing out large problems to disk (nlogn complexity) worse than gurobi handles reading and solving them (expn complexity). Then I end up writing out the problems into text files on python „by hand“, which is very cumbersome. I wish python and it’s libraries wasn’t so inefficient.
There is also https://www.python-mip.com/ which claims to be more efficient, but it supports fewer solvers than pulp, and I have not had opportunity to test it. Would anyone here have experience on how well their efficiency claims hold in practice?
Re: Linear Programming in Python (2023)
#4I have often started modeling problems with pulp. As the problem size gets bigger, pulp will fail to work —- that is, pulp handles writing out large problems to disk (nlogn complexity) worse than gurobi handles reading and solving them (expn complexity). Then I end up writing out the problems into text files on python „by hand“, which is very cumbersome. I wish python and it’s libraries wasn’t so inefficient.
Taking a look at pulp it just seems to be a solver abstraction API and all the real work is done by solver libraries written in lower-level languages. It looks like the default solver is COIN-OR CLP/CBC, and that's written in C++: https://github.com/coin-or/Cbc/tree/master/src
Maybe I'm misunderstanding something here and it's the abstraction API causing the problems, but it seems like it's up to the solver implementation to be efficient here?
Re: Linear Programming in Python (2023)
#5Fwiw, reinforcement learning offers objectively better optimization and more efficient inference than LP in many cases.
Re: Linear Programming in Python (2023)
#6I have often started modeling problems with pulp. As the problem size gets bigger, pulp will fail to work —- that is, pulp handles writing out large problems to disk (nlogn complexity) worse than gurobi handles reading and solving them (expn complexity). Then I end up writing out the problems into text files on python „by hand“, which is very cumbersome. I wish python and it’s libraries wasn’t so inefficient.
PuLP is a great way to get started, and I used it as my first attempt for many problems. However, I have since switched to using Google OR tools (https://developers.google.com/optimization/pack/bin_packing) or Python's Z3 interface, depending on the problem I'm working on.
Re: Linear Programming in Python (2023)
#7Fwiw, reinforcement learning offers objectively better optimization and more efficient inference than LP in many cases.
Any good examples or notebooks using RL to solve typical optimization problems?
Re: Linear Programming in Python (2023)
#8Fwiw, reinforcement learning offers objectively better optimization and more efficient inference than LP in many cases.
The cons are that RL sometimes requires more babysitting, more non-subject-matter (ie, optimization-method-specific) expertise, and can be a bit less... predictable... at inference time.
Also, LP often offers perfectly acceptable optimization and latency. The only case where this isn't true for offline problems is where LP times out or where relaxations of the problem are required to prevent timeout.
If I had to choose one method to learn, I guess I would learn RL. But it's a false choice. Given that I know both well, I'd reach for LP unless there was a good reason to use RL instead. And from a user perspective LP is much easier to learn than RL (see cons), so I think this is good general advice. Definitely worth having both tools on your belt if you plan on working on problems where either is a good fit.
Re: Linear Programming in Python (2023)
#9Fwiw, reinforcement learning offers objectively better optimization and more efficient inference than LP in many cases.
Any good examples or notebooks using RL to solve typical optimization problems?
I also am curious about this statement. If a problem becomes too complex for linear programming solutions, how easy is it to know that a reinforcement learning solution is actually a global optima and not just local?
Re: Linear Programming in Python (2023)
#10Fwiw, reinforcement learning offers objectively better optimization and more efficient inference than LP in many cases.
"reinforcement learning offers objectively better optimization"
I don't follow. Linear programming (with the simplex method) finds the optimal solution set for any linear program eventually. How can reinforcement learning be "better" apart from efficiency?