polysolve-python
Small Python binding for PolySolve's nonlinear and linear solver interfaces.
import numpy as np
import scipy.sparse
import polysolve
class Quadratic(polysolve.Problem):
def value(self, x):
y = x - np.array([-2.0, 3.0, 1.0])
return float(y @ y)
def gradient(self, x):
return 2.0 * (x - np.array([-2.0, 3.0, 1.0]))
def hessian(self, x):
return 2.0 * 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 available
x = solver.solve(b)
print(solver.info())
1 maintainer
Metadata
1 maintainer
Release files for polyfem-polysolve 0.0.1
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Built distributions (wheels)
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twine/6.1.0 CPython/3.13.12
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Signed by GitHub Actions, verified by PyPI on May 22, 2026.
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