An implementation of a steepest descent solver for binary quadratic
models.
Steepest descent is the discrete analogue of gradient descent, but
the best move is computed using a local minimization rather rather
than computing a gradient. At each step, we determine the dimension
along which to descend based on the highest energy drop caused by a
variable flip.
Optional building mode set with environment variables:
- TESTS=yes (performs tests, requires dimod)
This requires: python3-numpy
Maintained by: William PC
Keywords: quantum,quantum annealing,binary quadratic model,binary quadratic model solvers,dwave,d-wave
ChangeLog: dwave-greedy
Homepage:
https://github.com/dwavesystems/dwave-greedy
Download SlackBuild:
dwave-greedy.tar.gz
dwave-greedy.tar.gz.asc (FAQ)
(the SlackBuild does not include the source)
Individual Files: |
README |
dwave-greedy.SlackBuild |
dwave-greedy.info |
slack-desc |
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