Unconstrained Reduced-Space Quasi-Newton¶
-
class
kona.algorithms.
ReducedSpaceQuasiNewton
(primal_factory, state_factory, eq_factory, ineq_factory, optns=None)[source]¶ Bases:
kona.algorithms.base_algorithm.OptimizationAlgorithm
Unconstrained optimization using quasi-Newton in the reduced space, globalized using either back-tracking or Strong Wolfe line search on the objective as the merit function.
This algorithm can leverage both limited-memory BFGS and limited-memory Symmetric Rank 1 approximations of the Hessian.
Variables: - factor_matrices (bool) – Boolean flag for matrix-based PDE solvers.
- min_radius, max_radius (radius,) – Trust radius parameters.
- mu_init, mu_max, mu_pow, eta (mu,) – Augmented Lagrangian constraint factor parameters.
- grad_scale, feas_scale (scale,) – Optimality metric normalization factors.
- approx_hessian (
QuasiNewtonApprox
-like) – The quasi-Newton approximation object for the Hessian. - globalization (string) – Flag to determine solution globalization type.