# dwave-greedy¶

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.

>>> import greedy
...
>>> solver = greedy.SteepestDescentSolver()
>>> sampleset = solver.sample_ising({0: 2, 1: 2}, {(0, 1): -1})
...
>>> print(sampleset)
0  1 energy num_oc.
0 -1 -1   -5.0       1
['SPIN', 1 rows, 1 samples, 2 variables]


Code