pysolnp 2025.10.17

This provides the SOLNP optimization Algorithm.

See full documentation on http://solnp.readthedocs.io.

pysolnp - Nonlinear optimization with the augmented Lagrange method

Description

SOLNP solves the general nonlinear optimization problem on the form:

    minimize f(x)
      subject to
       g(x) = e_x
   l_h <= h(x) <= u_h
   l_x <   x   < u_X

where f(x), g(x) and h(x) are smooth functions.

Compatibility

Precompiled Wheels are available for CPython:

• Windows: Python 3.6+
• Linux: Python 3.6+
• Mac OS: Python 3.6+

For other systems, or to have BLAS and LAPACK support, please build the wheels manually. Note: For best results, building it from source is recommended, as BLAS and LAPACK will make a difference.

Installation

Simply install the package through PyPi with: pip install pysolnp

When compiling from source code you will need CMake.
See the README for the C++ code for details.

Usage

Below is the Box example, for the complete example see /python_examples/example_box.py.

import pysolnp

def f_objective_function(x):
    return -1 * x[0] * x[1] * x[2]

def g_equality_constraint_function(x):
    return [4 * x[0] * x[1] + 2 * x[1] * x[2] + 2 * x[2] * x[0]]

x_starting_point = [1.1, 1.1, 9.0]
x_l = [1.0, 1.0, 1.0]
x_u = [10.0, 10.0, 10.0]
e_x = [100]

result = pysolnp.solve(
    obj_func=f_objective_function,
    par_start_value=x_starting_point,
    par_lower_limit=x_l,
    par_upper_limit=x_u,
    eq_func=g_equality_constraint_function,
    eq_values=e_x)

result.solve_value
result.optimum
result.callbacks
result.converged

Output:

>>> result.solve_value
-48.11252206814995
>>> result.optimum
[2.8867750707815447, 2.8867750713194273, 5.773407748939196]
>>> result.callbacks
118
>>> result.converged
True

Parameters

The basic signature is:

solve(obj_func: function, par_start_value: List, par_lower_limit: object = None, par_upper_limit: object = None, eq_func: object = None, eq_values: object = None, ineq_func: object = None, ineq_lower_bounds: object = None, ineq_upper_bounds: object = None, rho: float = 1.0, max_major_iter: int = 10, max_minor_iter: int = 10, delta: float = 1e-05, tolerance: float = 0.0001, debug: bool = False) -> pysolnp.Result

Inputs:

Parameter	Type	Default value*	Description
obj_func	Callable[List, float]	-	The objective function f(x) to minimize.
par_start_value	List	-	The starting parameter x_0.
par_lower_limit	List	None	The parameter lower limit x_l.
par_upper_limit	List	None	The parameter upper limit x_u.
eq_func	Callable[List, float]	None	The equality constraint function h(x).
eq_values	List	None	The equality constraint values e_x.
ineq_func	Callable[List, float]	None	The inequality constraint function g(x).
ineq_lower_bounds	List	None	The inequality constraint lower limit g_l.
ineq_upper_bounds	List	None	The inequality constraint upper limit g_l.
rho	float	1.0	Penalty weighting scalar for infeasability in the augmented objective function.**
max_major_iter	int	400	Maximum number of outer iterations.
max_minor_iter	int	800	Maximum number of inner iterations.
delta	float	1e-07	Step-size for forward differentiation.
tolerance	float	1e-08	Relative tolerance on optimality.
debug	bool	False	If set to true some debug output will be printed.

*Defaults for configuration parameters are based on the defaults for Rsolnp.
**Higher values means the solution will bring the solution into the feasible region with higher weight. Very high values might lead to numerical ill conditioning or slow down convergence.

Output: The function returns the pysolnp.Result with the below properties.

Property	Type	Description
solve_value	float	The value of the objective function at optimum f(x*).
optimum	List[float]	A list of parameters for the optimum x*.
callbacks	int	Number of callbacks done to find this optimum.
converged	boolean	Indicates if the algorithm converged or not.
hessian_matrix	List[List[float]]	The final Hessian Matrix used by pysolnp.

Use-cases and Applications

• NMPC - Nonlinear model predictive controls-case studies using Matlab, REXYGEN and pysolnp NLP solver under Python environment by Štěpán Ožana. [NMPC Overhead Crane (PDF)] [GitHub Source Code]

Authors

• Krister S Jakobsson - Implementation - [e-mail]

License

This project is licensed under the Boost License - see the license file for details.

Acknowledgments

• Yinyu Ye - Publisher and mastermind behind the original SOLNP algorithm, Original Sources
• Alexios Ghalanos and Stefan Theussl - The people behind RSOLNP, Github repository
• Davis King - The mastermind behind Dlib, check out his blog! Blog