Amesos

Amesos is a set of C++ interfaces to serial and parallel sparse direct solvers. Amesos contains two nice sparse solvers: KLU and Paraklete. KLU is serial, while Paraklete (distributed with Trilinos 7.0 or higher) is a parallel solver. Amesos also offers an interface to LAPACK, and several other well-know solvers available on the web. The main idea of Amesos is to give a high-level view of direct solvers, as composed by four main phases: specification of parameters; initialization of the solver, using matrix sparsity only; computation of the factors; solution of the linear system. ...


References in zbMATH (referenced in 15 articles )

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  1. Kouri, D. P.: A matrix-free trust-region Newton algorithm for convex-constrained optimization (2022)
  2. Bui, Quan M.; Hamon, François P.; Castelletto, Nicola; Osei-Kuffuor, Daniel; Settgast, Randolph R.; White, Joshua A.: Multigrid reduction preconditioning framework for coupled processes in porous and fractured media (2021)
  3. Dissanayake, Chandani; Juan, Lourdes; Long, Kevin R.; Peace, Angela; Rana, Md Masud: Genotypic selection in spatially heterogeneous producer-grazer systems subject to stoichiometric constraints (2019)
  4. Fang, Rui; Kronbichler, Martin; Wurzer, Maximilian; Wall, Wolfgang A.: Parallel, physics-oriented, monolithic solvers for three-dimensional, coupled finite element models of Lithium-ion cells (2019)
  5. Shadid, J. N.; Pawlowski, R. P.; Cyr, E. C.; Tuminaro, R. S.; Chacón, L.; Weber, P. D.: Scalable implicit incompressible resistive MHD with stabilized FE and fully-coupled Newton-Krylov-AMG (2016)
  6. Dupraz, Marie; Filippi, Simonetta; Gizzi, Alessio; Quarteroni, Alfio; Ruiz-Baier, Ricardo: Finite element and finite volume-element simulation of pseudo-ECGs and cardiac alternans (2015)
  7. Bochev, Pavel; Lai, James; Olson, Luke: A locally conservative, discontinuous least-squares finite element method for the Stokes equations (2012)
  8. Howle, Victoria E.; Kirby, Robert C.: Block preconditioners for finite element discretization of incompressible flow with thermal convection. (2012)
  9. Shadid, J. N.; Pawlowski, R. P.; Banks, J. W.; Chacón, L.; Lin, P. T.; Tuminaro, R. S.: Towards a scalable fully-implicit fully-coupled resistive MHD formulation with stabilized FE methods (2010)
  10. Deparis, Simone; Rozza, Gianluigi: Reduced basis method for multi-parameter-dependent steady Navier-Stokes equations: applications to natural convection in a cavity (2009)
  11. Arbenz, Peter; van Lenthe, G. Harry; Mennel, Uche; Müller, Ralph; Sala, Marzio: A scalable multi-level preconditioner for matrix-free (\mu)-finite element analysis of human bone structures (2008)
  12. Deparis, Simone: Reduced basis error bound computation of parameter-dependent Navier-Stokes equations by the natural norm approach (2008)
  13. Sala, Marzio; Spotz, William F.; Heroux, Michael A.: PyTrilinos: High-performance distributed-memory solvers for Python (2008)
  14. Sala, Marzio; Stanley, Kendall S.; Heroux, Michael A.: On the design of interfaces to sparse direct solvers. (2008)
  15. Lin, Paul T.; Sala, Marzio; Shadid, John N.; Tuminaro, Ray S.: Performance of fully coupled algebraic multilevel domain decomposition preconditioners for incompressible flow and transport (2006)