pympcc 0.5.0

Python solver for Mathematical Programs with Complementarity Constraints (MPCC) using IPOPT via cyipopt

pympcc

A Python solver for Mathematical Programs with Complementarity Constraints (MPCC) using IPOPT via cyipopt, with an optional SciPy backend.

Six reformulation strategies, certified MPCC multipliers, B-stationarity / SOSC diagnostics, parametric sensitivity, and a jax.custom_vjp-registered solve for end-to-end differentiability.

Problem form

min  f(x)
s.t. g(x) ≤ 0              (inequality constraints)
     h(x) = 0              (equality constraints)
     G(x) ≥ 0  }
     H(x) ≥ 0  }           (complementarity)
     G(x)ᵀ H(x) = 0  }

Install

Requirements: Python ≥ 3.11. The IPOPT backend also requires a working IPOPT installation.

brew install ipopt                           # macOS
sudo apt-get install coinor-libipopt-dev     # Linux

pip install "pympcc[ipopt]"                  # default backend
pip install "pympcc[scipy]"                  # SciPy backend only
pip install "pympcc[jax]"                    # solve_jax + JAX-AD derivatives
pip install "pympcc[dev]"                    # for development

See the documentation for the optional custom IPOPT linear-solver bridge and source builds.

Quickstart

import numpy as np
import pympcc

# min (x0-2)² + (x1-1)²   s.t.  x0 ≥ 0 ⊥ x1 ≥ 0
problem = pympcc.MPCCProblem(
    n=2, n_comp=1,
    x0=np.array([0.5, 0.5]),
    xl=np.zeros(2),
    objective=lambda x: (x[0] - 2)**2 + (x[1] - 1)**2,
    gradient=lambda x: np.array([2*(x[0]-2), 2*(x[1]-1)]),
    comp_G=lambda x: np.array([x[0]]),
    comp_G_jacobian=lambda x: np.array([[1.0, 0.0]]),
    comp_H=lambda x: np.array([x[1]]),
    comp_H_jacobian=lambda x: np.array([[0.0, 1.0]]),
)

result = pympcc.solve(problem, strategy="scholtes")
print(result.x, result.obj, result.success)   # [2. 0.] 1.0 True

Strategies

Strategy	How it works	Best for
"direct"	G·H ≤ 0; single solve	Quick feasibility checks
"scholtes"	G·H ≤ ε, drives ε → 0	Default — most robust
"smoothing"	Fischer-Burmeister φ_ε(G,H) = 0, drives ε → 0	Tight complementarity residuals
"lin_fukushima"	G·H ≤ ε and G+H ≥ ε; preserves MPCC-MFCQ	Degenerate problems
"augmented_lagrangian"	PHR penalty in objective	Problems where MFCQ fails
"slack"	Lifts G, H to slacks; sparse comp rows	Large-n problems (n ≫ n_comp)

Highlights

• Certified stationarity — tnlp_refine=True re-solves a tightened NLP with the active set fixed and extracts MPCC-clean multipliers; classifies S- / W-stationarity. (docs)
• Diagnostics — MPCC-LICQ / MPCC-MFCQ check, B-stationarity by branch enumeration, MPCC-SOSC, multi-merit cross-check, condition numbers, degeneracy report. (docs)
• Parametric sensitivity — pympcc.sensitivity(result, problem, dgrad_L_dp=..., dc_dp=...) returns dx*/dp and dλ*/dp from a single KKT linear solve. (docs)
• Differentiable — pympcc.solve_jax(parametric, theta, ...) is jax.custom_vjp-registered: differentiate transparently through a converged MPCC. (docs)
• Bilevel — pympcc.bilevel.from_lower_level(...) compiles min_x F(x,y) s.t. y ∈ argmin_y f(x,y) to an MPCC by emitting lower-level KKT. (docs)
• Sparse-friendly — COO-format Jacobians end-to-end; the slack strategy keeps the comp block at 2·n_comp nonzeros regardless of n.
• AMPL .nl reader — pympcc.frontend.ampl.from_nl(path) for MacMPEC-format inputs.
• Multistart and presolve — pympcc.multistart(...), presolve=True.

Documentation

Full user guide, API reference, and theory primer at pympcc.readthedocs.io.

Testing

uv run pytest tests/ -v

The suite covers all six strategies, all diagnostic modules, the MacMPEC benchmark collection, sensitivity, and solve_jax. The direct strategy tests are marked xfail(strict=False) because LICQ generically fails at MPCC feasible points.

References

• Scholtes, S. (2001). Convergence properties of a regularization scheme for mathematical programs with complementarity constraints. SIAM J. Optim. 11(4), 918–936.
• Lin, G.-H., & Fukushima, M. (2003). New relaxation method for mathematical programs with complementarity constraints. J. Optim. Theory Appl. 118(1), 81–116.
• Fischer, A. (1992). A special Newton-type optimization method. Optimization 24(3–4), 269–284.
• Luo, Z.-Q., Pang, J.-S., & Ralph, D. (1996). Mathematical Programs with Equilibrium Constraints. Cambridge University Press.
• Leyffer, S. (2000). MacMPEC — AMPL collection of MPECs. Argonne National Laboratory.