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Can anyone point me to a tool (preferably in python or c++) to solve an optimization problem where in the objective has power fractions such as :
Maximize x1^0.2 + x2^ 0.3 + x3^0.4
Thanks
I think this should be possible in python with scipy.optimize
Since it allows fractional exponentiation
4**0.5 = 2
4**0.2 = 1.3195079
and the objective function is defined as a plain python function.
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I'm trying to get some approximation ratios for the Maximum Independent Set Problem and so I need some exact solutions !
I've found libraries written in C++ (i.e https://github.com/iPapatsoris/Maximum-Independent-Set)
but wondered if there were any directly in Python. I know of the `networkx' maximal indepedent set function but these are only approximations.
I realise it's far from the most efficient language to use but I'm only solving small Erdős–Rényi graphs (N<20).
In addition to this, are there any libraries that solve this for the weighted problem, where some nodes matter more than others?
This is the only python library I could find:
https://github.com/pchervi/Graph-Coloring/blob/master/Coloring_MWIS_heuristics.py
I haven't checked that it works correctly however.
I've been using KaMIS instead, which is a C++ implementation.
https://github.com/KarlsruheMIS/KaMIS
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Does anyone know of any tools to do a natural neighbor interpolation in python? unless i'm being stupid, i can't seem to find it in the scipy.interpolate module
Here is a 3D discrete natural neighbor implementation:
https://github.com/innolitics/natural-neighbor-interpolation
Note that the "discrete" is important here; it is an approximation of the true geometric natural neighbor.
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I have been using the Simple Blob Detection algorithm from the OpenCV library (for Python) for a research project. I would like to reference this particular method algorithm in my paper.
Does anyone know from where this method is from and indicate me a good to reference to cite? The openCV source code does not refer to any particular literature.
Thanks
It uses the Connected-component labeling algorithm.
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I have matrix differential equation diff(x) = A * x, x(0) = C, where:
x is 1 * N
A is N * N, and is a constant matrix.
I want to solve it with python. By the time I have found ways to get only approximate solutions, but I want to get an exact solution. What library can do it for me?
You can use sympy for symbolic mathematics in Python. In particular, look at sympy documentation on ODEs.
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I am looking for an implementation of Continuous Wavelet Transform for Python that includes Haar Wavelet.
I would like to reproduce the experiment given by MathWorks for Matlab, at this link.
I tried with Pyscellania but I obtain completely different coefficients.
Is there a Python implementation of the CWT out there that includes the Haar Wavalet apart from Pyscellania?
Your request is clear.
Have you tried Pyscellania's normalised or standard Haar Wavelet?
Maybe you are just using the wrong one.