Deep into the bowels of the Physical Sciences Library I went to
retrieve the Proceedings of a Symposium on the Complexity of Computer
Computation to track down Karp's famous paper for my last
. Later I discovered one of my fellow Chicago professors,
Janos Simon, attended that conference as a young grad student and
still had the proceeding in his office.
The symposium held March 20-22, 1972 at IBM Yorktown marked a shift in
theoretical computer science towards more algorithms and complexity
from logic, automata and grammars. The symposium attracted 225
registrants on par with our current STOC and FOCS conferences. From
The symposium dealt with complexity studies closely related to how
computations were actually performed on computers. Although this area
of study has not yet found an appropriate or generally accepted name,
the area is recognized by a significant commonality in problems,
approaches, and motivations. The area can be described and delineated
by examples such as the following.
- Determining lower bounds on the number of operations or steps
required for computational solutions of specific problems such as
matrix and polynomial calculations, sorting and other combinatorial
problems, iterative computations, solving equations, and computer
- Developing improved algorithms for the solution of such problems
which provide good upper bounds on the number of required operations,
along with experimental and theoretical evidence concerning the
efficiency and numerical accuracy of those algorithms.
- Studying the effects on the efficiency of computation brought
about by variations in sequencing and the introduction of parallelism.
- Studying the relative complexity of classes of problems with respect
to lower bounds on computation time. In this effort, specific problems
are classified as having equivalent difficulty of computation; for
example, those problem which can be solved in a number of operations
which is a polynomial function of the size of the input.
The symposium had a panel discussion on the state of the field, with a
panel that included four future Turing Award winners:
Robert Floyd, John Hopcroft, Richard Karp and Michael Rabin. The
proceedings has a transcription of the panel session. Here are some
Ray Miller (Moderator): There are four general questions.
Floyd responding to the first question: Slowly.
- How is the theory developing from originally being a scattering of
a few results on lower bounds and some algorithms into a more unified
- What specific examples have been found to demonstrate how real
computer computations were improved from studies of this type?
- Is the progress of numerical-type computations and understanding
of them much ahead of the combinatorial?
- Are there important open questions?
Karp: We need a to find a name for our subject. "Computational
Complexity" is too broad in view of the work of Blum and others,
at least until we can gather them into our fold; "Concrete
Computational Complexity" is too much like civil engineering;
"Complexity of Computer Computations" doesn't ring true;
Markov has already taken "theory of algorithms" away from
us…Getting these materials into university curricula, particularly the
undergraduate curriculum, is important and it's going to happen
There are lots of things that computer people do that aren't
mathematical in the nineteenth century sense. We manipulate strings,
we prove theorems, we retrieve information, we manipulate algebraic
formulas. Looking at the primitives appropriate to these domains, we
can get a much richer class of problems.
Charles Fiduccia: Working against unification is the failure of
specifying a model for the computation. There seems to be too much
emphasis on what is being computed and little emphasis on the model.
Hopcroft (responding to Floyd): You could change "slowly" to
"it's not". Karp has shown many of these problems complete,
the implication being that therefore these problems are hard. That
might not be the case, they could even be algorithms that run in
linear time. On the other hand, the fact that we know so little about
lower bounds provides a lot of interest in the area.
Albert Meyer: The obstacle is not that we don't have a good
formulation of a machine model. We have many different models,
but we can't prove lower bounds for any of them.
Mike Patterson: Suppose somebody were to prove P≠NP then this would
be of great personal and dramatic interest. But if it were to be
established by a quite traditional, ordinary argument, which is just
happened that nobody had thought of, then we would see in retrospect
that it was the wrong problem.
Numerical and combinatorial algorithmic people, with some small
exceptions, never did integrate well. But Blum and the computational
complexity people did come "into the fold" and algorithms
and complexity would come to dominate theoretical computer science in
the US. The naming issue never did have a satisfactory solution, the
Europeans call it Theory
. And I would be very happy with a "traditional
ordinary" proof of P≠NP but I strongly doubt the
solution is so simple.
As a side note, had I been able to find Karp's paper online I would
never have read about this symposium that marked the new direction of
theoretical computer science research.