  Kawamura and Cook specified the least set of information about a continuous
function on the unit interval which is needed for fast function evaluation.
This paper presents a variation of their result. To make the above statement
precise, one has to specify what a "set of information" is and what "fast"
should mean. Kawamura and Cook use polynomial-time computability in the sense
of second-order complexity theory to define what "fast" means but do not use
the most general "sets of information" this framework is able to handle.
Instead they require codes to be length-monotone. This paper removes the
additional premise of length-monotonicity, and instead imposes further
conditions on the speed of the evaluation: The operation should now be
computable in "hyper-linear" time. This means that the running time can not
contain any iterations of the length function and, while an arbitrary
polynomial may be applied to its value, on the argument side at most a shift by
a constant is allowed. This is a very restrictive notion, but one can check
that the Kawamura and Cook representation allows for hyper-linear time
evaluation. The paper proves that it is not minimal with this property by
providing the minimal set of information necessary for hyper-linear evaluation
and proving that it is not polynomial-time equivalent to any encoding using
only length-monotone names. Ultimatively, this is due to a failure of
polynomial-time computability of an upper bound to a modulus of continuity.
Indeed this failure seems to reflect the behavior of software based on the
ideas of computable analysis appropriately and was one of the reasons for a
closer investigation in the first place.
