A real # is computable if its left cut, L###; is computable. If #q i # i is a computable sequence of rationals computably converging to #; then fq i g; the corresponding set, is always computable. A computably enumerable #c.e.# real # is a real which is the limit of an increasing computable sequence of rationals, and has a left cut which is c.e. We study the Turing degrees of representations of c.e. reals, that is the degrees of increasing computable sequences converging to #: For example, every representation A of # is Turing reducible to L###: Every noncomputable c.e. real has both a computable and noncomputable representation. In fact, the representations of noncomputable c.e. reals are dense in the c.e. Turing degrees, and yet not every c.e. Turing degree below deg T L### necessarily contains a representation of #: 1 Introduction Computability theory essentially studies the relative computability of sets of natural numbers. Since G#odel introduced a method for coding s...

We investigate the structure of EXP and NEXP complete and hard sets under various kinds of reductions. In particular, we are interested in the way in which information that makes the set complete is stored in the set. To address this question for a given hard set A, we construct a sparse set S, and ask whether A \Gamma S is still hard. It turns out that for most of the reductions considered and for an arbitrary given sparseness condition, there is a single subexponential time computable set S that meets this condition, such that A \Gamma S is not hard for any A. Not only is this set S subexponential time computable, but a slight modification of the construction can make the complexity of S meet any reasonable superpolynomial function. On the other hand we show that for any polynomial-time computable sparse set S, the set A \Gamma S remains hard. There are other properties than time complexity that make a set `almost' polynomial-time computable. For sparse p-selective sets...

We show that if every NP set is polynomial-time bounded truthtable reducible to some P-selective set, then NP is contained in DTIME(2 n O(1= p log n) ). In the proof, we implement a recursive procedure that reduces the number of nondeterministic steps of a given nondeterministic computation.

The set of minimal indices of a G#del numbering ' is deøned as MIN' = fe : (8i ! e)[' i 6= 'e ]g. It has been known since 1972 that MIN' jT ; 00 , but beyond this MIN' has remained mostly uninvestigated. This thesis collects the scarce results on MIN' from the literature and adds some new observations including that MIN' is autoreducible, but neither regressive nor (1; 2)- computable. We also study several variants of MIN' that have been deøned in the literature like size-minimal indices, shortest descriptions, and minimal indices of decision tables. Some challenging open problems are left for the adventurous reader. 1 Introduction How long is the shortest program that solves your problem? There are at least two ways to interpret this question depending on the type of problem involved. If the program's task is to output one speciøc object, we are looking for a shortest description of that object. This interpretation is closely related to Kolmogorov complexity. Although we have sev...

There have been several papers over the last ten years that consider the number of queries needed to compute a function as a measure of its complexity. The following function has been studied extensively in that light: F A a (x 1 ; : : : ; x a ) = A(x 1 ) 1 1 1 A(x a ): We are interested in the complexity (in terms of the number of queries) of approximating F A a . Let b a and let f be any function such that F A a (x 1 ; : : : ; x a ) and f(x 1 ; : : : ; x a ) agree on at least b bits. For a general set A we have matching upper and lower bounds that depend on coding theory. These are applied to get exact bounds for the case where A is semirecursive, A is superterse, and (assuming P 6= NP) A = SAT. We obtain exact bounds when A is the halting problem using different methods. 1 Introduction The complexity of a function can be measured by the number of queries (to some oracle) needed to compute it. This notion has been studied in both a 3 Dept. of Computer Sc...

A query-bounded Turing machine is an oracle machine which computes its output function from a bounded number of queries to its oracle. In this paper we investigate the behavior of nondeterministic query-bounded Turing machines. In particular we study how easily such machines can compute the function F A n (x 1 , . . . , x n ) from A, where A # N and F A n (x 1 , . . . , x n ) = ##A (x 1 ), . . . , #A (x n )#. We show that each truth-table degree contains a set A such that, F A n can be nondeterministically computed from A by asking at most one question per nondeterministic branch; and that every set of the form A # also has this property. On the other hand, we show that if A is a 1-generic set then F A n cannot be nondeterministically computed from A in less that n queries to A; and that each non-zero r.e. Turing degree contains an r.e. set A with the same property. If the machines involved can only make queries that are part of their input then all sets such that F A n ca...

This paper is dedicated to Chris Ash, who invented α-systems. Abstract. A model is computable if its domain is a computable set and its relations and functions are uniformly computable. Let A be a computable model and let R be an extra relation on the domain of A. That is, R is not namedinthelanguageofA. Wedefine DgA(R) to be the set of Turing degrees of the images f(R) under all isomorphisms f from A to computable models. We investigate conditions on A and R which are sufficient and necessary for DgA(R) to contain every Turing degree. These conditions imply that if every Turing degree ≤ 0 00 can be realized in DgA(R) via an isomorphism of the same Turing degree as its image of R, thenDgA(R) contains every Turing degree. We also discuss an example of A and R whose DgA(R) coincides with the Turing degrees which are ≤ 0 0. 1. Introduction and

Introduction One of the important questions in computational complexity theory is whether every NP problem is solvable by polynomial time circuits, i.e., NP `?P=poly. Furthermore, it has been asked what the deterministic time complexity of NP is if NP ` P=poly. That is, if NP is easy in the nonuniform complexity measure, how easy is NP in the uniform complexity measure? Let P T (SPARSE) be the class of languages that are polynomial time Turing reducible to some sparse sets. Then it is well known that P T (SPARSE) = P=poly. Hence the above question is equivalent to the following question. NP `?PT (SPARSE): It has been shown by Wilson [18] that thi

Abstract. We study the classes of hypersimple and semicomputable sets as well as their intersection in the weak truth table degrees. We construct degrees that are not bounded by hypersimple degrees outside any non-trivial upper cone of Turing degrees and show that the hypersimple-free c.e. wtt degrees are downwards dense in the c.e. wtt degrees. Moreover, we consider the sets that are both hypersimple and semicomputable, characterize them as the (bi-infinite) c.e. cuts of computable orderings of N of order type ω+ω ∗ and study their wtt degrees. We show that there are hypersimple degrees that are not bounded by any hypersimple semicomputable degree, investigate relationships with the join and look for maximal and minimal elements of related classes. 1.

We identify two properties that for P-selective sets are effectively computable. Namely we show that, for any P-selective set, finding a string that is in a given length's top Toda equivalence class (very informally put, a string from Σ^n that the set's P-selector function declares to be most likely to belong to the set) is FP computable, and we show that each P-selective set contains a weakly-P -rankable subset.