xipopt
xipopt solves nonlinear optimization problems written as JAX functions with Ipopt.
A problem is a pytree of variables, an objective and constraints, all plain JAX functions. xipopt compiles the function values and their derivatives into programs for the PJRT C API through cyxla. Ipopt's callbacks execute those programs directly from Cython, so a solve never enters the Python interpreter. The API follows JAX-CIP.
Installation
pip install xipopt
The wheels contain Ipopt with the MUMPS and SPRAL linear solvers, so nothing else needs installing. They exist for Linux, macOS on Apple silicon and Windows, on Python 3.11 and later.
Example
This is problem 71 of Hock and Schittkowski, which the Ipopt documentation uses as its example.
import jax.numpy as jnp
import xipopt as xip
def objective(x, params):
return x[0] * x[3] * (x[0] + x[1] + x[2]) + x[2]
problem = xip.Problem(
variables=xip.Variable(lower=1.0, upper=5.0, shape=(4,)),
objective=objective,
constraints=(
xip.Constraint(lambda x, params: jnp.prod(x), lower=25.0),
xip.Constraint(lambda x, params: jnp.sum(x**2), lower=40.0, upper=40.0),
),
)
solution = xip.solve(problem, jnp.array([1.0, 5.0, 5.0, 1.0]))
print(solution.status, solution.objective, solution.x)
assert solution.status is xip.Status.SOLVE_SUCCEEDED
assert abs(solution.objective - 17.0140173) < 1e-6
Documentation
The documentation has a user guide, an explanation of the design and the API reference.
License
Hippocratic License 3.0. The wheels bundle libraries under their own licenses, which licenses lists.
1 maintainer
Metadata
1 maintainer
Release files for xipopt 0.1.0
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