RN-SLRA is a geometry-adaptive regularized Newton method for manifold-affine intersections that guarantees local linear convergence under intrinsic transversality and quadratic convergence under transversality with residual-dependent regularization.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
The no-barber principle prohibits selection rules in the inaccessible game that appeal to external adjudicators, favoring the symmetric monoidal category NCFinProb over the cartesian FinProb as its internal language due to the absence of canonical copying maps.
citing papers explorer
-
A Geometry-Adaptive Regularized Newton-Type Method for Manifold-Affine Intersection Problems
RN-SLRA is a geometry-adaptive regularized Newton method for manifold-affine intersections that guarantees local linear convergence under intrinsic transversality and quadratic convergence under transversality with residual-dependent regularization.
-
The No Barber Principle: Towards Formalised Selection in the Inaccessible Game
The no-barber principle prohibits selection rules in the inaccessible game that appeal to external adjudicators, favoring the symmetric monoidal category NCFinProb over the cartesian FinProb as its internal language due to the absence of canonical copying maps.