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VoLCA — Life Cycle Assessment engine
Copyright (c) 2024-present Christophe Combelles and contributors

Licensed under the Apache License, Version 2.0 (see LICENSE).

==============================================================================
This product includes third-party software. A detailed inventory with full
copyright notices and license texts is in THIRD_PARTY_LICENSES.md and at the
runtime endpoint /api/v1/licenses.

The notable third-party components are:

  * MUMPS 5.8.1 (sequential sparse direct solver) — CeCILL-C
    https://mumps-solver.org/

  * mumps-hs (Haskell FFI bindings to MUMPS) — Apache-2.0

  * PORD (ordering library bundled with MUMPS) — public domain

  * AMD ordering routines and *_TRUNCATED_RRQR variants (bundled with MUMPS)
    — BSD 3-Clause

  * BLAS, LAPACK (linked at runtime) — BSD 3-Clause

  * Numerous Haskell libraries — predominantly BSD 3-Clause and MIT
    (full table in THIRD_PARTY_LICENSES.md)

==============================================================================
MUMPS — required CeCILL-C copyright notice
==============================================================================

MUMPS is a software application for the direct solution of large sparse
systems of linear equations on distributed memory parallel computers.

  Copyright 1991–2024 CERFACS, CNRS, ENS Lyon, INP Toulouse, Inria,
  Mumps Technologies, University of Bordeaux.

This software is governed by the CeCILL-C license under French law and abiding
by the rules of distribution of free software. You can use, modify and/or
redistribute the software under the terms of the CeCILL-C license as
circulated by CEA, CNRS and Inria at http://www.cecill.info.

The unmodified MUMPS source code is available from the upstream project at
https://mumps-solver.org/. CeCILL-C §5.3.1 entitles you to request it at no
more than the cost of transfer.

When citing MUMPS in academic work, please reference:

  [1] P. R. Amestoy, I. S. Duff, J.-Y. L'Excellent and J. Koster.
      A fully asynchronous multifrontal solver using distributed dynamic
      scheduling. SIAM Journal on Matrix Analysis and Applications,
      Vol 23, No 1, pp 15–41 (2001).

  [2] P. R. Amestoy, A. Buttari, J.-Y. L'Excellent and T. Mary.
      Performance and scalability of the block low-rank multifrontal
      factorization on multicore architectures. ACM Transactions on
      Mathematical Software, Vol 45, Issue 1, Article 2, pp 1–26 (2019).