Toolbox for quadratic binary optimization
Project description
qubolite
A light-weight toolbox for working with QUBO instances in NumPy.
Installation
pip install qubolite
This package was created using Python 3.10, but runs with Python >= 3.8.
Optional Dependencies
If you're planning to use the roof dual function as lower bound you will need to install optional
dependencies. The igraph based roof dual lower bound function can be used by calling
qubolite.bounds.lb_roof_dual()
. It requires that the igraph library is
installed. This can be done with pip install igraph
or by installing qubolite with
pip install qubolite[roof_dual]
.
Using the function qubolite.ordering_distance()
requires the Kendall-τ measure from the
scipy library which can be installed by pip install scipy
or by installing
qubolite with pip install qubolite[kendall_tau]
.
If you would like to install all optional dependencies you can use pip install qubolite[all]
for
achieving this.
Usage Examples
By design, qubolite
is a shallow wrapper around numpy
arrays, which represent QUBO parameters.
The core class is qubo
, which receives a numpy.ndarray
of size (n, n)
.
Alternatively, a random instance can be created using qubo.random()
.
>>> import numpy as np
>>> from qubolite import qubo
>>> arr = np.triu(np.random.random((8, 8)))
>>> Q = qubo(arr)
>>> Q2 = qubo.random(12, distr='uniform')
By default, qubo()
takes an upper triangle matrix.
A non-triangular matrix is converted to an upper triangle matrix by adding the lower to the upper triangle.
To get the QUBO function value, instances can be called directly with a bit vector.
The bit vector must be a numpy.ndarray
of size (n,)
or (m, n)
.
>>> x = np.random.random(8) < 0.5
>>> Q(x)
7.488225478498116
>>> xs = np.random.random((5,8)) < 0.5
>>> Q(xs)
array([5.81642745, 4.41380893, 11.3391062, 4.34253921, 6.07799747])
Version Log
- 0.2 Added problem embeddings (binary clustering, subset sum problem)
- 0.3 Added
QUBOSample
class and sampling methodsfull
andgibbs
- 0.4 Renamed
QUBOSample
toBinarySample
; added methods for saving and loading QUBO and Sample instances - 0.5 Moved
gibbs
tomcmc
and implemented true Gibbs sampling asgibbs
; addednumba
as dependency- 0.5.1 changed
keep_prob
tokeep_interval
in Gibbs sampling, making the algorithm's runtime deterministic; renamedsample
torandom
in QUBO embedding classes, added MAX 2-SAT problem embedding
- 0.5.1 changed
- 0.6 Changed Python version to 3.8; removed
bitvec
dependency; addedscipy
dependency required for matrix operations in numba functions- 0.6.1 added scaling and rounding
- 0.6.2 removed
seedpy
dependency - 0.6.3 renamed
shots
tosize
inBinarySample
; cleaned up sampling, simplified type hints - 0.6.4 added probabilistic functions to
qubo
class - 0.6.5 complete empirical prob. vector can be returned from
BinarySample
- 0.6.6 fixed spectral gap implementation
- 0.6.7 moved
brute_force
to new sub-modulesolving
; added some approximate solving methods - 0.6.8 added
bitvec
sub-module;dynamic_range
now uses bits by default, changedbits=False
todecibel=False
; removed scipy from requirements - 0.6.9 new, more memory-efficient save format
- 0.6.10 fixed requirements in
setup.py
; fixed size estimation inqubo.save()
- 0.7 Added more efficient brute-force implementation using C extension; added optional dependencies for calculating bounds and ordering distance
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