We offer a short tour into the interactive interpretation of sequential programs. We emphasize streamlike computation — that is, computation of successive bits of information upon request. The core of the approach surveyed here dates back to the work of Berry and the author on sequential algorithms on concrete data structures in the late seventies, culminating in the design of the programming language CDS, in which the semantics of programs of any type can be explored interactively. Around one decade later, two major insights of Cartwright and Felleisen on one hand, and of Lamarche on the other hand gave new, decisive impulses to the study of sequentiality. Cartwright and Felleisen observed that sequential algorithms give a direct semantics to control operators like call-cc and proposed to include explicit errors both in the syntax and in the semantics of the language PCF. Lamarche (unpublished) connected sequential algorithms to linear logic and games. The successful program of games semantics has spanned over the nineties until now, starting with syntax-independent characterizations of the term model of PCF by Abramsky, Jagadeesan, and Malacaria on one hand, and by Hyland and Ong on the other hand.
Two important models of functions that have been constructed since turned out to be the extensional collapse (i.e. the hereditary quotient equating sequential algorithms computing the same function, i.e. (in the affine case) two algorithms @math and @math such that @math ): Bucciarelli and Ehrhard's model of strongly stable functions @cite_3 @cite_16 , and Longley's model of sequentially realizable functionals @cite_10 . The first model arose from an algebraic characterization of sequential (first-order) functions, that carries over to all types. The second one is a realizability model over a combinatory algebra in which the interaction at work in sequential algorithms is encoded.
@cite_16 @cite_10 @cite_3
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We prove that, in the hierarchy of simple types based on the type of natural numbers, any finite strongly stable function is equal to the application of the semantics of a PCF-definable functional to some strongly stable (generally not PCF-definable) functionals of type two. Applying a logical relation technique, we derive from this result that the strongly stable model of PCF is the extensional collapse of its sequential algorithms model. We study various models of classical second-order set theories in the dependent type theory of Coq. Without logical assumptions, Aczel’s sets-as-trees interpretation yields an intensional model of second-order ZF with functional replacement. Building on work of Werner and Barras, we discuss the need for quotient axioms in order to obtain extensional models with relational replacement and to construct large sets. Specifically, we show that the consistency strength of Coq extended by excluded middle and a description operator on well-founded trees allows for constructing models with exactly n Grothendieck universes for every natural number n. By a previous categoricity result based on Zermelo’s embedding theorem, it follows that those models are unique up to isomorphism. Moreover, we show that the smallest universe contains exactly the hereditarily finite sets and give a concise independence proof of the foundation axiom based on permutation models. Mulmuley [Mul12a] recently gave an explicit version of Noether’s Normalization lemma for ring of invariants of matrices under simultaneous conjugation, under the conjecture that there are deterministic black-box algorithms for polynomial identity testing (PIT). He argued that this gives evidence that constructing such algorithms for PIT is beyond current techniques. In this work, we show this is not the case. That is, we improve Mulmuley’s reduction and correspondingly weaken the conjecture regarding PIT needed to give explicit Noether Normalization. We then observe that the weaker conjecture has recently been nearly settled by the authors ([FS12]), who gave quasipolynomial size hitting sets for the class of read-once oblivious algebraic branching programs (ROABPs). This gives the desired explicit Noether Normalization unconditionally, up to quasipolynomial factors. As a consequence of our proof we give a deterministic parallel polynomial-time algorithm for deciding if two matrix tuples have intersecting orbit closures, under simultaneous conjugation. We also study the strength of conjectures that Mulmuley requires to obtain similar results as ours. We prove that his conjectures are stronger, in the sense that the computational model he needs PIT algorithms for is equivalent to the well-known algebraic branching program (ABP) model, which is provably stronger than the ROABP model. Finally, we consider the depth-3 diagonal circuit model as defined by Saxena [Sax08], as PIT algorithms for this model also have implications in Mulmuley’s work. Previous work (such as [ASS12] and [FS12]) have given quasipolynomial size hitting sets for this model. In this work, we give a much simpler construction of such hitting sets, using techniques of Shpilka and Volkovich [SV09]. Summary form only. Inspired by Kolmogorov's structure function for finite sets as models of data in the algorithmic theory of information we adapt the construct to families of probability models to avoid the noncomputability problem. The picture of modeling looks then as follows: The models in the family have a double index, where the first specifies a structure, ranging over a finite or a countable set, and the second consists of parameter values, ranging over a continuum. An optimal structure index can be determined by the MDL (Minimum Description Length) principle in a two-part code, where the sum of the code lengths for the structure and the data is minimized. The latter is obtained from the universal NML (Normalized Maximum Likelihood) model for the subfamily of models having a specified structure. The determination of the optimal model in the optimized structure is more difficult. It requires a partition of the parameter space into equivalence classes, each associated with a model, in such a way that the Kullback-Leibler distance between any two adjacent models is equal and that the models are optimally distinguishable from the given amount of data. This notion of distinguishability is a modification of a related idea of Balasubramanian. The particular model, specified by the observed data, is the simplest one that incorporates all the properties in the data that can be extracted with the model class considered.