solve(self, trust_radius)
This algorithm requires function values and first and second derivatives. It also performs a costly Hessian decomposition for most iterations, and the Hessian is required to be positive definite.
The Hessian is required to be positive definite.
We are allowed to wander only this far away from the origin.
The proposed step.
True if the proposed step is on the boundary of the trust region.
Minimize a function using the dog-leg trust-region algorithm.
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