Modules in Package SODEPACK

Package SODEPACK (Downloadable)

SLSODA

Solves initial-value problems for stiff and nonstiff systems of ordinary differential equations. Automatically selects between Adams (nonstiff) and Backward Differentiation Formula (stiff) methods. In the stiff case, the Jacobian matrix may be full or banded, and either user-supplied or internally approximated by difference quotients. Resulting linear systems solved by direct methods (LU factor/solve). (By: A.C. Hindmarsh and L.R. Petzold).

SLSODAR

Solves initial-value problems for stiff and nonstiff systems of ordinary differential equations. Automatically selects between Adams (nonstiff) and Backward Differentiation Formula (stiff) methods. In the stiff case, the Jacobian may be full or banded, and either user-supplied or internally approximated. Resulting linear systems solved by direct methods. Has rootfinding capability. (By: A.C. Hindmarsh and L.R. Petzold).

SLSODE

Solves initial-value problems for stiff and nonstiff systems of ordinary differential equations. Both Adams (nonstiff), and Backward Differentiation Formula (stiff) methods are used. In the stiff case, the Jacobian matrix may be full or banded, and either user-supplied or internally approximated by difference quotients. The resulting linear systems solved by direct methods (LU factor/solve). (By: A.C. Hindmarsh).

SLSODES

Solves initial-value problems for stiff and nonstiff systems of ordinary differential equations. Both Adams (nonstiff), and Backward Differentiation Formula (stiff) methods are used. In the stiff case, it treats the Jacobian matrix in general sparse form, with the sparsity structure determined on its own or by the user. The Yale Sparse Matrix Package is used to solve the linear systems that arise. (By: A.C. Hindmarsh and A.H. Sherman).

SLSODI

Solves linearly-implicit initial-value problems for stiff and nonstiff systems of ordinary differential equations. Both Adams (nonstiff), and Backward Differentiation Formula (stiff) methods are used. All matrices may be full or banded, and the Jacobian may be either user-supplied or internally approximated by difference quotients. The resulting linear systems solved by direct methods (LU factor/solve). (By: A.C. Hindmarsh and J.F. Painter).

SLSODIS

Solves linearly-implicit initial-value problems for stiff and nonstiff systems of ordinary differential equations. Both Adams (nonstiff), and Backward Differentiation Formula (stiff) methods are used. All matrices are assumed to be sparse, with the sparsity structure determined on its own or by the user. Uses parts of the Yale Sparse Matrix Package to solve the linear systems that arise, by a direct method. (By: A.C. Hindmarsh and S. Balsdon).

SLSODKR

Solves initial-value problems for stiff and nonstiff systems of ordinary differential equations. Selects between Adams and BDF methods. Resulting linear systems solved by a selection of four preconditioned Krylov (iterative) solvers. User must supply a pair of routine to evaluate, preprocess, and solve the preconditioner matrices. Option for a user-supplied linear system solver to use without Krylov iteration and rootfinding capability. (By: Hindmarsh and Brown).

SLSODPK

Solves initial-value problems for stiff and nonstiff systems of ordinary differential equations. Selects between Adams and BDF methods. Resulting linear systems solved by a selection of four preconditioned Krylov (iterative) solvers. User must supply a pair of routine to evaluate, preprocess, and solve the (left and/or right) preconditioner matrices. Option for a user-supplied linear system solver to use without Krylov iteration.(By: A.C. Hindmarsh and Peter N. Brown).

SLSOIBT

Solves linearly-implicit initial-value problems for stiff and nonstiff systems of ordinary differential equations. Both Adams (nonstiff), and Backward Differentiation Formula (stiff) methods are used. All matrices are assumed to be block-tridiagonal, and the Jacobian may be either user-supplied or internally approximated. The resulting linear systems solved by direct methods (LU factor/solve). (By: A.C. Hindmarsh and C.S. Kenney).