Dissociation and span #
This file defines dissociation and span of sets in groups. These are analogs to the usual linear
independence and linear span of sets in a vector space but where the scalars are only allowed to be
0 or ±1. In characteristic 2 or 3, the two pairs of concepts are actually equivalent.
Main declarations #
MulDissociated/AddDissociated: Predicate for a set to be dissociated.Finset.mulSpan/Finset.addSpan: Span of a finset.
A set is dissociated iff all its finite subsets have different products.
This is an analog of linear independence in a vector space, but with the "scalars" restricted to
0 and ±1.
Instances For
A set is dissociated iff all its finite subsets have different sums.
This is an analog of linear independence in a vector space, but with the "scalars" restricted to
0 and ±1.
Instances For
Alias of the reverse direction of mulDissociated_inv.
Alias of the forward direction of mulDissociated_inv.
The span of a finset s is the finset of elements of the form ∏ a ∈ s, a ^ ε a where
ε ∈ {-1, 0, 1} ^ s.
This is an analog of the linear span in a vector space, but with the "scalars" restricted to
0 and ±1.
Equations
- s.mulSpan = Finset.image (fun (ε : α → ℤ) => ∏ a ∈ s, a ^ ε a) (Fintype.piFinset fun (_a : α) => {-1, 0, 1})
Instances For
The span of a finset s is the finset of elements of the form ∑ a ∈ s, ε a • a
where ε ∈ {-1, 0, 1} ^ s.
This is an analog of the linear span in a vector space, but with the "scalars" restricted to
0 and ±1.
Equations
- s.addSpan = Finset.image (fun (ε : α → ℤ) => ∑ a ∈ s, ε a • a) (Fintype.piFinset fun (_a : α) => {-1, 0, 1})
Instances For
If every dissociated subset of s has size at most d, then s is actually generated by a
subset of size at most d.
This is a dissociation analog of the fact that a set whose linearly independent subsets all have
size at most d is of dimension at most d itself.
If every dissociated subset of s has size at most d, then s is actually
generated by a subset of size at most d.
This is a dissociation analog of the fact that a set whose linearly independent subspaces all have
size at most d is of dimension at most d itself.