QRSlv: Difference between revisions
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'''QRSlv''' is the primary [[NLA]] solver for ASCEND. It can solve systems of nonlinear algebraic equation, but it is not suitable for [[optimisation]] problems containing [[MAXIMIZE]] or [[MINIMIZE]] statements (for those, [[IPOPT]] or [[CONOPT]] are suggested). It is also not suitable for [[conditional modelling]] problems. | '''QRSlv''' is the primary [[NLA]] solver for ASCEND. It can solve systems of nonlinear algebraic equation, but it is not suitable for [[optimisation]] problems containing [[MAXIMIZE]] or [[MINIMIZE]] statements (for those, [[IPOPT]] or [[CONOPT]] are suggested). It is also not suitable for [[conditional modelling]] problems. | ||
QRSlv divides a system into precedence-ordered 'blocks' which are solved one-by-one using a powerful Newton-style iterative solver. It provides a number of algorithms for scaling and reordering the equations for efficient solution. | QRSlv divides a system into [[block partitioning|precedence-ordered 'blocks']] which are solved one-by-one using a powerful Newton-style iterative solver. It provides a number of algorithms for scaling and reordering the equations for efficient solution. | ||
Add here: problem definition. | Add here: problem definition. | ||
Revision as of 05:19, 3 February 2011
| NLA |
|---|
| QRSlv |
| CMSlv |
| IPSlv |
| NLP |
| CONOPT |
| IPOPT |
| TRON |
| MINOS |
| Opt |
| NGSlv |
| DAE/ODE |
| IDA |
| LSODE |
| DOPRI5 |
| RADAU5 |
| LA |
| Linsolqr |
| Linsol |
| LP |
| MakeMPS |
| Logic |
| LRSlv |
QRSlv is a nonlinear algebraic equation solver based on the paper A Modified Least Squares Algorithm for Solving Sparse NxN Sets of Nonlinear Equations by A. Westerberg and S. Director doi:10.1016/0098-1354(78)80011-8.
QRSlv is the primary NLA solver for ASCEND. It can solve systems of nonlinear algebraic equation, but it is not suitable for optimisation problems containing MAXIMIZE or MINIMIZE statements (for those, IPOPT or CONOPT are suggested). It is also not suitable for conditional modelling problems.
QRSlv divides a system into precedence-ordered 'blocks' which are solved one-by-one using a powerful Newton-style iterative solver. It provides a number of algorithms for scaling and reordering the equations for efficient solution.
Add here: problem definition.
Add here: sample problems.