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These were only defined for `BlockTensorMap`/`BlockTensorMap` and `SparseBlockTensorMap`/`SparseBlockTensorMap` pairs, so a mixed pair fell through to TensorKit's generic `AbstractTensorMap` methods, which operate on the fused per-sector `BlockMatrix`es. That path throws a `BoundsError` whenever a coupled sector has an interior zero-length block, which is the normal situation for a `SumSpace` with heterogeneous summands. Replace the specializations by a single `AbstractBlockTensorMap` method each, branching on `issparse` only where the cases differ. Also drop the dense-only `axpy!`, `axpby!`, `dot` and `mul!`, which reimplement TensorKit generics that forward to this layer (the deleted `mul!` referenced `SparseArrayKit._zero!`, which is not a dependency of this package). Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
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Fixes #74.
add!,scale!(y, x, α)andinnerwere only defined for dense/dense and sparse/sparse pairs; a mixed pair fell through to TensorKit's generic methods, which broadcast over the fused per-sectorBlockMatrixes and hit aBoundsErrorfor coupled sectors with an interior zero-length block.AbstractBlockTensorMapmethod each instead of the per-storage specializations, branching onissparseonly where neededaxpy!/axpby!/dot/mul!: they reimplement TensorKit generics that forward here (the deletedmul!calledSparseArrayKit._zero!, not a dependency)add!into a sparse destination from a dense source fills it in;dropzeros!afterwards if needed🤖 Generated with Claude Code