polysolve-python

Small Python binding for PolySolve's nonlinear and linear solver interfaces.

importnumpyasnpimportscipy.sparseimportpolysolveclassQuadratic(polysolve.Problem):
defvalue(self, x):
y=x-np.array([-2.0, 3.0, 1.0])
returnfloat(y @ y)
defgradient(self, x):
return2.0* (x-np.array([-2.0, 3.0, 1.0]))
defhessian(self, x):
return2.0*scipy.sparse.eye(x.size, format="csc")
x, log=polysolve.minimize(
Quadratic(),
np.zeros(3),
{
"solver": "Newton",
"line_search": {"method": "Backtracking"},
"max_iterations": 100,
},
{"solver": "Eigen::SimplicialLDLT"},
)
print(x)
print(log)

Python subclasses must implement value(x), gradient(x), and hessian(x). Optional PolySolve callbacks such as solution_changed, stop, is_step_valid, and max_step_size can also be implemented on the subclass.

Linear solves

For a one-off linear system:

A=scipy.sparse.csc_matrix([[4.0, 1.0], [1.0, 3.0]])
b=np.array([1.0, 2.0])
x=polysolve.solve(A, b, {"solver": "Eigen::SimplicialLDLT"})

For repeated solves with the same matrix pattern:

solver=polysolve.LinearSolver({"solver": "Eigen::SimplicialLDLT"})
solver.analyze_pattern(A)
solver.factorise(A) # factorize(A) is also availablex=solver.solve(b)
print(solver.info())