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Acknowledgments

POUNCE’s nonlinear-programming core is a Rust port of Ipopt, the interior-point nonlinear programming solver by Andreas Wächter, Lorenz T. Biegler, and the COIN-OR community. Its algorithm, console output, and option semantics are modeled directly on that codebase, which is released under the EPL-2.0.

It is a sibling of ripopt, an earlier memory-safe interior-point NLP optimizer in Rust by the same author (DOI 10.5281/zenodo.19542664).

Convex solver inspiration

The specialized convex conic solver (pounce-convex; see Convex Solver) is a pure-Rust port of ideas — not a wrapper — from two reference projects, gratefully acknowledged:

  • Clarabel by Paul Goulart and Yuwen Chen (University of Oxford). POUNCE’s homogeneous-free conic interior-point design — a quadratic objective handled directly over a product of symmetric cones, with Nesterov–Todd scaling for the second-order cone and a diagonal-plus-rank-1 sparse KKT representation — follows Clarabel’s approach. Clarabel is itself a pure-Rust solver; POUNCE shares the spirit but is an independent implementation.
  • PaPILO, the presolving library of SCIP (the Zuse Institute Berlin optimization suite). POUNCE’s transaction-stack presolve with full primal and dual postsolve — forcing constraints, dominated columns, bound tightening with global dual recovery, parallel/duplicate rows, iterated to a fixpoint — is modeled on PaPILO’s catalog and postsolve discipline.

Contributors

  • David Bernal Neira (@bernalde) designed and prototyped the auxiliary-equality preprocessing pass in ripopt PR #32. POUNCE’s pounce-presolve::auxiliary Phase-0 orchestrator (issue #53) is a port of that work — Hopcroft-Karp matching, Dulmage-Mendelsohn partition, Tarjan SCC, block-triangular reduction, damped-Newton block solver, reduction frame with multiplier recovery — and ships with the tutorial_flow_density{,_perturbed}.nl and gaslib11_steady.nl test fixtures David vendored.
  • Milan Rother (@milanofthe) suggested the boundary value problem solver and the tritium gas-liquid-contactor (GLC) test problem behind docs/src/bvp.md and python/examples/glc_feral_vs_scipy.py. The GLC model is adapted from pathsim-chem (src/pathsim_chem/tritium/glc.py, MIT License, Copyright (c) 2025 PathSim).

Key references

  • Wächter, A., Biegler, L.T. “On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming.” Mathematical Programming 106(1), 25–57 (2006). DOI 10.1007/s10107-004-0559-y — the algorithm POUNCE implements.
  • Wächter, A., Biegler, L.T. “Line search filter methods for nonlinear programming: Motivation and global convergence.” SIAM Journal on Optimization 16(1), 1–31 (2005). DOI 10.1137/S1052623403426556
  • Wächter, A., Biegler, L.T. “Line search filter methods for nonlinear programming: Local convergence.” SIAM Journal on Optimization 16(1), 32–48 (2005). DOI 10.1137/S1052623403426544
  • Fletcher, R., Leyffer, S. “Nonlinear programming without a penalty function.” Mathematical Programming 91(2), 239–269 (2002). DOI 10.1007/s101070100244 — the filter concept underlying the line search.
  • Pirnay, H., López-Negrete, R., Biegler, L.T. “Optimal sensitivity based on IPOPT.” Mathematical Programming Computation 4(4), 307–331 (2012). DOI 10.1007/s12532-012-0043-2 — the sIPOPT method behind pounce-sensitivity.
  • Duff, I.S. “MA57—a code for the solution of sparse symmetric definite and indefinite systems.” ACM Transactions on Mathematical Software 30(2), 118–144 (2004). DOI 10.1145/992200.992202 — the optional ma57 linear-solver backend.
  • Goulart, P.J., Chen, Y. “Clarabel: An interior-point solver for conic programs with quadratic objectives.” (2024). arXiv:2405.12762 / Clarabel.rs — the conic interior-point design behind pounce-convex.
  • Gleixner, A., Gottwald, L., Hoen, A. “PaPILO: A Parallel Presolving Library for Integer and Linear Optimization with Multiprecision Support.” INFORMS Journal on Computing 35(6), 1329–1341 (2023). DOI 10.1287/ijoc.2022.0171 — the presolve catalog and dual-postsolve model behind pounce-convex::presolve.
  • Domahidi, A., Chu, E., Boyd, S. “ECOS: An SOCP solver for embedded systems.” European Control Conference (2013), 3071–3076. DOI 10.23919/ECC.2013.6669541 — the sparse second-order-cone KKT representation.
  • Amos, B., Kolter, J.Z. “OptNet: Differentiable Optimization as a Layer in Neural Networks.” ICML (2017), 136–145. arXiv:1703.00443 — the implicit differentiation behind the pounce.jax convex layers.
  • Wilkinson, M.D. et al. “The FAIR Guiding Principles for scientific data management and stewardship.” Scientific Data 3, 160018 (2016). DOI 10.1038/sdata.2016.18 — the provenance model behind the JSON solve report.