osqp | The Operator Splitting QP Solver | Machine Learning library

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geopandas 0.10.1

• have noted that your data is on kaggle, so start by sourcing it
• there really is only one issue shapely.geometry.MultiPoint() constructor does not work with a filtered series. Pass it a numpy array instead and it works.
• full code below, have randomly selected a point to serve as gpdPoint

Unfortunately, the message is generated by GLPK 4.65, regardless of the GLP_MSG_* option used.

However, it appears that version 5.0 has fixed this, see this glpk-help mailing-list message from the library maintainer:

To fix the bug please replace lines 923-930 in glpk/src/draft/glpios03.c

I tried to fool OQSP (in python) to give your result.

I first suspected of alpha but it seems to make no difference. The other thing is the number of iterations, it could have not converged after 25 iterations, but in that case I would not expect an objective of 0.70000.

You can install a version of osqp < 0.6.2, which does not depend on qdldl-python to install cvxpy.

Without running this (code is obviously incomplete), there are two candidates:

• A: OSQP
• B: Internal PSD-checks within cvxpy (before submitting data to solvers!)

I'm guessing it's B (and you might switch to some other solver to reason about it).

The iteration-parameter you are setting is related to the solvers and if i'm guessing correctly, you are not even reaching this point (error in canonicalization-phase).

It seems, your matrix induces trouble in regards to iterative eigenvalue-computations (ARPACK). Maybe this can be seen by it's condition-number.

Not sure, if there is an easy transformation / perturbation for your use-case to improve the conditioning here.

Lots of internals are not easily accessable without rebuilding cvxpy from sources, but in this case you might get away by changing cvxpy.settings.settings.EIGVAL_TOL:

First,

I have numpy installed - in my python 3.8.1 terminal, I can import numpy with no problem.

tox creates a virtual environment when you run it so it doesn't matter if you have numpy installed on your computer original interpreter.

Second, If you want tox to install the requirements file you must add deps = -rrequirements.txt to tox.ini. And you can always add numpy manually as a dependency there as well.

Third, tox has some dependencies tracking issues in some cases. Try run tox -r to force tox to recreate its environment and make sure that numpy mentioned in the "install_requires" section in the setup.py.

I was finally able to install cvxpy successfully. I found this github issue very helpful. I ended up install Python 3.8 (64-bit) instead of (32-bit) which I had by accident. I installed

(numpy+mkl, scipy, cvxopt, scs, ecos, and osqp) in that order and all from downloaded wheels except for scs which I did using pip install scs I then did pip install nose and then nosetests cvxpy and go the following output: