| |
|
|
filterSQP
The solver filterSQP solves large, sparse or dense linear, quadratic and nonlinear programming problems.
Main features
-
The solver filterSQP
is a Sequential Quadratic Programming solver
suitable for solving
large, sparse or dense linear,
quadratic and nonlinear programming problems.
The method avoids the use of penalty functions.
Global convergence is enforced through the use of a trust--region and the
new concept of a ''filter'' which accepts a trial point whenever the
objective or the constraint violation is improved compared to all
previous iterates. The size of the trust--region is reduced if the step
is rejected and increased if it is accepted
(provided the agreement between the quadratic model and the nonlinear
functions is sufficiently good).
The QP subproblems are solved using bqpd.
-
filterSQP
needs second order information for the objective function and the
nonlinear constraints.
Tomlab estimates any unknown derivatives, but the accuracy and convergence
may be worse in such cases.
-
The dense and the sparse version of
filterSQP
are compiled in two different Mex binaries,
making the two versions optimally efficient.
-
filterSQP
is integrated with the TOMLAB driver routines.
-
filterSQP
may be used as subproblem solver in the TOMLAB
environment.
|
|
