[WIP] Named-operator constructors, projection verbs, and balanced gram_eigh_full#229
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[WIP] Named-operator constructors, projection verbs, and balanced gram_eigh_full#229mtfishman wants to merge 9 commits into
mtfishman wants to merge 9 commits into
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Match TensorAlgebra's switch to A ≈ X * X' for gram_eigh_full: relabel X with the domain dimension names (rank trailing) so it mirrors V_R from eigh. The companion left inverse Y from gram_eigh_full_with_pinv likewise carries the domain names with the rank axis leading. Pin the matching TensorAlgebra branch via [sources] until that patch is registered.
- `Base.one(::AbstractNamedDimsArray, codomain, domain)` and `Base.one(::AbstractNamedDimsOperator)` build identity-shaped named arrays/operators by dispatching to `TensorAlgebra.one` on the underlying raw storage. The result sits in canonical (codomain, domain) name order. - `similar_operator(prototype, [T,] axes, [codomain_names,] domain_names)` allocates an operator with the user-supplied side as the domain and the codomain derived by `conj`-ing the domain axes; codomain names default to fresh `randname` outputs. Exported. - `Random.randn!` / `Random.rand!` peel down to the concrete storage, working around the ITensor `eltype(::Type) === Any` issue.
Previously `domain(::Bijection)` returned `values(::OrderedDict)`, a `Base.ValueIterator` that compares by object identity. Switching it to `keys` of the reverse dict gives a `Base.KeySet` whose `==` matches elementwise, so `domainnames(op1) == domainnames(op2)` does what one would expect. The two dicts are constructed from the same pairs in the same order, so `codomain(b)[i]` and `domain(b)[i]` remain in lock-step positional order. Also reworks the `Base.one` docstring examples to use `apply` (which renames the codomain back to the domain), replacing the matricize + identity-matrix check that's better suited to tests than docs.
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Centralizes the linear-map allocation primitive in TensorAlgebra so backends only need to overload `similar_map`, while `similar_operator` keeps the named-axis wrapping. Also drops the redundant `[sources.TensorAlgebra]` pin from `docs/`, `examples/`, and `test/Project.toml`; the workspace root `Project.toml` already pins the upstream feature branch for all subprojects.
mtfishman
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`TensorAlgebra.similar_map` now flips the domain direction itself when laying out storage (ITensor/TensorAlgebra.jl#177), so the wrapper passes both sides in the same direction and lets the primitive handle the bra/ket flip.
Without this, `Base.conj(::AbstractArray{<:Real})` short-circuits on a `NamedUnitRange` (element type `NamedInteger{Int, Name}` is a `Real` subtype) and returns the wrapper unchanged — never touching the inner range. That's a no-op for plain ranges but wrong for graded axes, where the inner conj needs to flip sector arrows.
`Base.conj`, `Base.:*(::, ::Number)`, `Base.:/(::, ::Number)`, and `LinearAlgebra.normalize` are overloaded for `AbstractNamedDimsArray` to delegate to the underlying array rather than the default broadcast path. Broadcast on a `NamedDimsArrayStyle` reaches into the storage via scalar `getindex`, which is unsupported on block-structured arrays such as `AbelianGradedArray` (and would round-trip through scalar slots even when the storage could handle the op block-wise). `denamed(a, inds)` also short-circuits when the requested order already matches `dimnames(a)`, returning `denamed(a)` directly. The previous path always wrapped in a `PermutedDimsArray` for the identity permutation, which hid the storage type from downstream dispatch (`LinearAlgebra.dot` is the visible case). Co-authored-by: Claude <noreply@anthropic.com>
`TensorAlgebra.projectto!(::AbstractNamedDimsArray, ::AbstractArray)` delegates to the underlying-storage `projectto!`, so a named operator allocated via `similar_map(prototype, T, named_codomain, named_domain)` can be filled from raw dense data uniformly across backends. The named layer doesn't need to know which storage the named array wraps. Co-authored-by: Claude <noreply@anthropic.com>
Mirrors the existing `projectto!` forwarder, so backend specializations (e.g. `AbelianGradedArray`'s materialize-and-compare check) are reachable through the named layer with their tolerance defaults intact. Co-authored-by: Claude <noreply@anthropic.com>
mtfishman
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Summary
Base.one(::AbstractNamedDimsOperator)andBase.one(::AbstractNamedDimsArray, codomain, domain)similar_operator(prototype, [T,] axes, [codomain_names,] domain_names), routed throughTensorAlgebra.similar_mapRandom.randn!andRandom.rand!onAbstractNamedDimsOperatorBase.conj(::AbstractNamedUnitRange), forwarded through the underlying range so graded-axis conjugation flips sector arrowsgram_eigh_fullaligned with the balanced convention from Balanced gram_eigh_full convention, add one and operator-construction primitives TensorAlgebra.jl#177domain(::Bijection)returns aBase.KeySet, so equal-pairBijections compare equalBase.conj,Base.:*/Base.:/by a scalar, andLinearAlgebra.normalizeonAbstractNamedDimsArray, routed throughdenamedto avoid the broadcast-scalar-indexing pathTensorAlgebra.projectto!andTensorAlgebra.checked_projectto!forwarded to the underlying storageTODO
[sources]pin onTensorAlgebraonce the matching version (0.9.5) registers.