Weak Ascending Set (Wu's Definition) #
This file implements the theory and algorithm for Weak Ascending Set, which are central to Wu's Method. In this context, a "Weak Ascending Set" requires that the initial of every element is reduced with respect to preceding elements. This is a weaker condition than the standard reduction.
Consequently, the algorithm for computing a Weak Basic Set must ensure the triangular structure
(strict ascending main variables) explicitly,
by filtering candidates with B.max_vars < p.max_vars.
Main declarations #
AscendingSetTheory: Implements the theory usinginitial.reducedTo(weak reduction).HasBasicSet: Provides thebasicSetalgorithm that computes a minimal weak ascending set.
The weak ascending set theory uses weak reduction p.initial.reducedTo.
Equations
- WeakAscendingSet.instAscendingSetTheory = { reducedTo' := fun (p : MvPolynomial σ R) => p.initial.reducedTo, decidableReducedTo := inferInstance, initial_reducedToSet_of_max_vars_ne_bot := ⋯ }
Instances For
The recursive algorithm for computing the Weak Basic Set.
Equations
- One or more equations did not get rendered due to their size.
- WeakAscendingSet.basicSet.go [] BS hl1_2 hl2_2 = BS
Instances For
Computes the Weak Basic Set of a list of polynomials.
Difference from Standard:
The filter condition includes B.max_vars < p.max_vars.
This is because p.initial.reducedTo B does NOT imply B.max_vars < p.max_vars
(unlike strong reduction).
We must enforce the triangular structure explicitly.
Equations
- One or more equations did not get rendered due to their size.
Instances For
The Weak Basic Set algorithm satisfies the abstract HasBasicSet interface.
Equations
- One or more equations did not get rendered due to their size.