TorchLean API

FloatLib.Floats.Formats.BinaryInterchange.Reduction.Tree

Finite, repeatedly rounded binary sums #

Each internal node uses Model.add, with one nearest-even rounding at that node. A finite execution certificate excludes exceptional inputs and overflow at every intermediate result. The error budget then follows from FloatLib's half-ULP theorem, including subnormal values. This API does not change Model.sum, which accumulates exactly and rounds only once.

All leaves and intermediate additions have real denotations.

Instances For

    The rounded real interpretation agrees with encoded execution at each finite node.

    The sum of half-ULPs at the actual intermediate additions.

    Instances For

      No normal-range hypothesis is needed: each finite addition has an absolute half-ULP bound.

      Two finite schedules over the same inputs can differ by at most their combined budgets.