Polynomial Maps
Cross-source consensus on Polynomial Maps from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Benefits
Comparisons
Highlighted claims
- The Euler discretization turns a polynomial ODE into a polynomial map by storing the step size as a coordinate. — Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs
- A primitive recursive threshold on step size is chosen so that Euler simulation stays within the robust output margin. — Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs
- Step-size-controlled polynomial maps use a fixed rational polynomial map with an externally supplied dyadic step size and primitive recursive thresholds. — Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs
- Fixed discrete polynomial maps cannot uniformly provide the autonomous continuous mechanisms available to polynomial ODEs. — Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs
- The step-size-controlled polynomial model is presented as a promising strictly polynomial framework where numerical resolution is explicit. — Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs