Unconstrained STCG-based RSNK¶
-
class
kona.algorithms.
UnconstrainedRSNK
(primal_factory, state_factory, eq_factory, ineq_factory, optns=None)[source]¶ Bases:
kona.algorithms.base_algorithm.OptimizationAlgorithm
A reduced-space Newton-Krylov optimization algorithm for PDE-governed unconstrained problems.
This algorithm uses a 2nd order adjoint formulation to compute matrix-vector products with the Reduced Hessian.
The product is then used in a Krylov solver to compute a Newton step.
The step can be globalized using either line-search or trust-region methods. The Krylov solver changes based on the type of globalization selected by the user. Unglobalized problems are solved via FGMRES, while trust-region and line-search methods use Conjugate-Gradient.
Note
Insert inexact-Hessian paper reference here.
Variables: - factor_matrices (bool) – Boolean flag for matrix-based PDE solvers.
- iter (int) – Optimization iteration counter.
- hessian (
ReducedHessian
) – Matrix object defining the Hessian matrix-vector product. - precond (
BaseHessian
-like) – Matrix object defining the approximation to the Hessian inverse. - krylov (
FGMRES
orSTCG
) – A krylov solver object used to solve the system defined by the Hessian. - globalization (string) – Flag to determine which type of globalization to use.
- max_radius (radius,) – Trust radius parameters.
- line_search (
BackTracking
) – Back-tracking line search tool. - merit_func (
ObjectiveMerit
) – Simple objective as merit function.